随机森林原理及应用

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1、ChanChanDF-XgeDF-XgePPwwClicktobuyNOW!ClicktobuyNOW!wmwmwowo.d.c.d.cocu-trackocu-track䱣ᴎỂᵫϔǃ䱣ᴎỂᵫㅔҟ䱣ᴎỂᵫߎ⦄ⱘ㚠᱃އㄪᷥއㄪᷥᅲ䰙Ϟᰃᇚぎ䯈⫼䍙ᑇ䴶䖯㸠ߦߚⱘϔ⾡ᮍ⊩ˈ↣⃵ߚࡆⱘᯊ׭ˈ䛑ᇚᔧࠡⱘぎ䯈ϔߚЎѠˈ↨བ䇈ϟ䴶ⱘއㄪᷥ˄Ѡঝᷥ˅˖Ѡঝᷥᇚ䆁㒗ḋᴀⱘߚࡆⱘ䖛⿟ℸᯊˈᇍᑨⱘѠ㓈ᑇ䴶ᇚ㹿ߚ៤བϟⱘḋᄤ˖㗗㰥ᇍ䈵᠔೼ぎ䯈ⱘߦߚއㄪᷥ䖭⾡ㅫ⊩᳝ⴔᕜ໮㡃དⱘ⡍ᗻˈ↨བ䇈䆁㒗ᯊ䯈໡ᴖᑺ䕗Ԣˈ乘⌟ⱘChanC

2、hanDF-XgeDF-XgePPwwClicktobuyNOW!ClicktobuyNOW!wmwmwowo.d.c.d.cocu-trackocu-track䖛⿟↨䕗ᖿ䗳ˈ῵ൟᆍᯧሩ⼎˄ᆍᯧᇚᕫࠄⱘއㄪᷥخ៤೒⠛ሩ⼎ߎᴹ˅ㄝDŽԚᰃৠᯊˈऩއㄪᷥজ᳝ϔѯϡདⱘഄᮍˈ↨བ䇈ᆍᯧover-fittingˈ㱑✊᳝ϔѯᮍ⊩ˈབ࠾ᵱৃҹޣᇥ䖭⾡ᚙމˈԚᰃ䖬ᰃϡ໳ⱘDŽ㒘ড়῵ൟ㒘ড়῵ൟ˄↨བ䇈᳝BaggingˈBoostingㄝ˅ϢއㄪᷥⳌ݇ⱘㅫ⊩↨䕗໮ˈ䖭ѯㅫ⊩᳔㒜ⱘ㒧ᵰᰃ⫳៤N(ৃ㛑Ӯ᳝޴ⱒỉҹϞ˅ỉᷥˈ䖭

3、ḋৃҹ໻໻ⱘޣᇥऩއㄪᷥᏺᴹⱘ↯⮙ˈ᳝⚍㉏ԐѢϝϾ㟁ⲂࣴㄝѢϔϾ䇌㨯҂ⱘخ⊩ˈ㱑✊䖭޴ⱒỉއㄪᷥЁⱘ↣ϔỉ䛑ᕜㅔऩ˄ⳌᇍѢC4.5䖭⾡ऩއㄪᷥᴹ䇈˅ԚᰃҪӀ㒘ড়䍋ᴹ⹂ᰃᕜᔎ໻DŽ೒1˖෎Ѣއㄪᷥⱘ㒘ড়῵ൟBaggingㅫ⊩Bagging˄BootstrapAGGregatING˅ᰃBreimanᦤߎⱘˈ݊ᗱᛇᰃᇍ䆁㒗䲚᳝ᬒಲഄᢑপ䆁㒗ḋ՟ˈҢ㗠Ў↣ϔϾ෎ᴀߚ㉏఼䛑ᵘ䗴ߎϔϾ䎳䆁㒗䲚ৠḋ໻ᇣԚ৘ϡⳌৠⱘ䆁㒗䲚ˈ䖯㗠䆁㒗ߎϡৠⱘ෎ᴀߚ㉏఼DŽBaggingㅫ⊩བ೒㸼᠔⼎˖ChanChanDF-XgeDF-X

4、gePPwwClicktobuyNOW!ClicktobuyNOW!wmwmwowo.d.c.d.cocu-trackocu-trackBaggingᰃ෎Ѣᇍ䆁㒗䲚䖯㸠໘⧚ⱘ䲚៤ᮍ⊩Ё᳔ㅔऩǃ᳔Ⳉ㾖ⱘϔ⾡DŽՓ⫼Baggingㅫ⊩ⱘᯊ׭ˈ⧚䆎Ϟ↣Ͼ෎ᴀߚ㉏఼ⱘ䆁㒗䲚Ё᳝63.2%ⱘ䞡໡ḋ՟DŽBreimanᣛߎˈ㽕ՓᕫBagging᳝ᬜˈ෎ᴀߚ㉏఼ⱘᄺдㅫ⊩ᖙ乏ᰃϡ〇ᅮⱘˈгህᰃ䇈ᇍ䆁㒗᭄᥂ᬣᛳDŽ෎ᴀߚ㉏఼ⱘᄺдㅫ⊩ᇍ䆁㒗᭄᥂䍞ᬣᛳˈBaggingⱘᬜᵰ䍞དˈ಴ℸᇍѢއㄪᷥ੠ҎᎹ⼲㒣㔥㒰䖭ḋⱘᄺдㅫ⊩B

5、aggingᰃⳌᔧ᳝ᬜⱘDŽ঺໪ˈ⬅ѢBaggingㅫ⊩ᴀ䑿ⱘ⡍⚍ˈՓᕫBaggingㅫ⊩䴲ᐌ䗖ড়⫼ᴹᑊ㸠䆁㒗໮Ͼ෎ᴀߚ㉏఼ˈ䖭гᰃBaggingㅫ⊩ⱘϔ໻Ӭ࢓DŽ䙷МՓ⫼Baggingㅫ⊩ⱘᯊ׭ᑨ䆹⫼໮ᇥϾ෎ᴀߚ㉏఼ড়䗖ਸ਼˛Breimanᣛߎ෎ᴀߚ㉏఼᭄Ⳃᑨᔧ䱣ⴔߚ㉏⾡᭄๲໮㗠๲ࡴDŽBaggingㅫ⊩೼㦋ᕫᮄḋᴀᯊ䞛⫼ⱘᰃ䱣ᴎᬒಲᢑḋˈ䖭ⳌᔧѢܜ䱣ᴎഄᬍবḋᴀᅲ՟ⱘᴗ䞡㗠㦋ᕫϔϾᮄⱘ䆁㒗䲚ˈ✊ৢ೼䖭Ͼ䆁㒗䲚ϟ䆁㒗ߎϔϾᔅߚ㉏఼DŽ㗠ᰒ✊೼ᵘ䗴ϟϔϾߚ㉏఼ᯊˈᑨ䆹䞡⚍㗗㰥㹿ࠡ䴶䙷ѯߚ㉏఼ߚ㉏䫭䇃ⱘḋᴀˈ㗠

6、ϡᰃᇍ᠔᳝ⱘḋᴀ䛑ϔḋⳟᕙDŽгे䙷ѯߚ䫭ⱘᅲ՟೼ᢑḋᯊᑨ䆹㹿ᢑⱘ⃵᭄᳈໮DŽ෎Ѣ䖭⾡ᛇ⊩ˈߎ⦄њBoostingㅫ⊩DŽBoostingㅫ⊩Boosting᳔ܜ⬅RobertT.Schapireᦤߎˈ݊ᗱᛇᰃᇍ↣䙷ѯᆍᯧߚ㉏䫭ⱘ䆁㒗ᅲ՟ࡴᔎᄺдˈህདڣϔϾҎ㚠㣅䇁ऩ䆡ϔḋˈ佪ܜ㄀ϔ䘡㚠ᅠҹৢ᳝ϔѯᆍᯧ䆄ԣⱘऩ䆡ህ䆄ԣњˈ䖬᳝ϔѯϡᆍᯧ䆄ԣⱘˈ߭㄀Ѡ䘡ⱘᯊ׭ᇍ䙷ѯϡᆍᯧ䆄ԣⱘऩ䆡໮ⳟ޴ⴐˈ㄀ϝ䘡জᇍ㄀Ѡ䘡䖬䆄ϡԣⱘऩ䆡ݡ໮ⳟ޴ⴐ……݋ԧᅲᮑⱘᯊ׭Boostingᮍ⊩ᰃ䖭ḋ䖯㸠ⱘ˖佪ܜ㒭↣ϔϾ䆁㒗ḋ՟䌟ќⳌ

7、ৠⱘᴗ䞡ˈ✊ৢ䆁㒗㄀ϔϾ෎ᴀߚ㉏఼ᑊ⫼ᅗᴹᇍ䆁㒗䲚䖯㸠⌟䆩ˈᇍѢ䙷ѯߚ㉏䫭䇃ChanChanDF-XgeDF-XgePPwwClicktobuyNOW!ClicktobuyNOW!wmwmwowo.d.c.d.cocu-trackocu-trackⱘ⌟䆩ḋ՟ᦤ催݊ᴗ䞡˄ᅲ䰙ㅫ⊩Ёᰃ䰡Ԣߚ㉏ℷ⹂ⱘḋ՟ⱘᴗ䞡˅ˈ✊ৢ⫼䇗ᭈৢⱘᏺᴗ䆁㒗䲚䆁㒗㄀ѠϾ෎ᴀߚ㉏఼ˈ✊ৢ䞡໡䖭Ͼ䖛⿟Ⳉࠄ᳔ৢᕫࠄϔϾ䎇໳དⱘᄺд఼DŽBoostingᰃϔ⾡ᦤ催ӏᛣ㒭ᅮᄺдㅫ⊩ޚ⹂ᑺⱘᮍ⊩DŽᅗⱘᗱᛇ䍋⑤ѢValiantᦤߎⱘPAC(P

8、robablyApproximatelyCorrect)ᄺд῵ൟDŽValiant੠Kearnsᦤߎњᔅᄺд੠ᔎᄺдⱘὖᗉ,䆚߿䫭䇃⥛ᇣѢ1/2,гेޚ⹂⥛ҙ↨䱣ᴎ⣰⌟⬹催ⱘᄺдㅫ⊩⿄Ўᔅᄺдㅫ⊩;䆚߿ޚ⹂⥛ᕜ催ᑊ㛑೼໮乍ᓣᯊ䯈ݙᅠ៤ⱘᄺдㅫ⊩⿄Ўᔎᄺдㅫ⊩DŽৠᯊ,Valiant੠Kearns佪⃵ᦤߎњPACᄺд῵ൟЁᔅᄺдㅫ⊩੠ᔎᄺдㅫ⊩ⱘㄝӋᗻ䯂乬,ेӏᛣ㒭ᅮҙ↨䱣ᴎ⣰⌟⬹དⱘᔅᄺдㅫ⊩,ᰃ৺ৃҹᇚ݊ᦤछЎᔎᄺдㅫ⊩?བᵰѠ㗙ㄝӋ,䙷Мা䳔ᡒࠄϔϾ↨䱣ᴎ⣰⌟⬹དⱘᔅ