Boosting and Ensemble Methods

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1 Boosng and Ensmbl Mhods

2 PAC Larnng modl Som dsrbuon D ovr doman X Eampls: <, c*> c* s h arg funcon Goal: Wh hgh probably -d fnd h n H such ha rrorh,c* < d and ar arbrarly small. Inro o ML 2

3 Wak Larnng - Confdnc Assum w ar gvn a larnng algorhm wh confdnc d ½ bu arbrary small accuracy ε>0 Can w boos h confdnc? How?! Inro o ML 3

4 BoosConfdnc Algorhm Inpu: Algorhm A and paramr d Cra klog2/d ndpndn problms Sampl S for -h copy Run Algorhm A on S wh accuracy /3 L h b h hypohss ha A oupus on S Tak a nw sampl S of sz m -2 log2k/d Rurn h bs hypohss h on S call h* Inro o ML 4

5 BoosConfdnc Analyss S For ach copy : Th probably ha rror h < /3 a las ½ Th probably ha som h has rrorh < /3 a las -2 -k - d/2 Holds for klog2/d Assum hs holds! Namly, som h has rrorh < /3 dno by h Inro o ML 5 +

6 BoosConfdnc Analyss Wh probably a las - d/2 for vry h : rrorh - [obs-rrorh ] ε/3 Chrnoff bound + unon bound usng m -2 log2k/d Assum hs holds! Toghr, wh probably a las -d rrorh* Errorh + ε/3 Obs-Errorh + 2ε/3 Obs-Errorh * Obs-Errorh + 2ε/3 Errorh * Obs_Error h * + ε/3 ε Inro o ML 6

7 Wak Larnng - Accuracy Assum: rrorh,c* < ½-g Th paramr g>0 s small consan Inuvly: gng slghly br han random should b asy Quson: Assum C s wak larnabl, s C PAC srong larnabl? Inro o ML 7

8 Wak Larnng - Dfnon Wak Larnng Algo A wak larns C usng H f ss γ>0 for all c n C for any dsrbuon D for all δ> 0 Oupus h n H such ha wh prob - δ rrorh,c < ½ - γ Srong Larnng Algo A srong larns C usng H f for all ε>0 for all c n C for any dsrbuon D for all δ> 0 Oupus h n H such ha wh prob - δ rrorh,c < ε Inro o ML 8

9 Wak larnng dfnon Why do w nd ANY dsrbuon Eampl: Consdr h followng dsrbuon ovr bs f 2 0 hn c* hard funcon ohrws c*0 Unform Dsrbuon Prdcng random for 2 0, 0 lswhr wll b corrc 87.5% Gng abov ha s mpossbl. Th hard dsrbuon: Sampl from h s whr 2 0 Inro o ML 9

10 Thr Wak Larnrs On wak larnr only on hng o do! Two wak larnrs wha o do f hy dsagr? Thr wak larnrs Can w mprov accuracy? Eampl - y - y Wak larnrs: sngl bs Inro o ML 0

11 Thr wak larnrs Frs wak larnr us h dsrbuon D g h Scond wak larnr How can w forc nw h? S D 2 s.. h has rror ½ G h 2 why h 2 h? Thrd wak larnr Wha ar nrsng npus? h 2 h L D 3 b such npus g h 3 How wll w prdc? If h 2 h usng h 3 Els h 2 or h majory Inro o ML

12 3 Wak Larnrs - Prformanc Dfn D 2 and D 3 Ds D 2 : Slc random b{0,} If b0 Sampl from D unl h c* Els b Sampl from D unl h c* Ds D 3 Sampl from D unl h h 2 Inro o ML Formally 0.5 D f - p D2 0.5 D f p p Pr[h c* ] D D3 Z 0 Z Pr[h h 2 f f ] h h h h c* h c* 2 h 2 2

13 3 Wak Larnrs - Analyss Assum, for smplcy, ha all WL hav rror ra p½ -γ If all rrors ar ndpndn, h rror of h majory s Error 3p 2 -p+p 3 3p 2-2p 3 Goal: holds vn for dpndn rrors Inro o ML 3

14 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ To convr from a probably n D 2 o D 0.5 D f - p D2 0.5 D f p p Pr[h c* ] h h c* c* Corrc n h : 2-p Error n h : 2p Inro o ML 4

15 c: 2-p : 2p 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P h on D h on D 2 h 2 on D 2 Inro o ML 5

16 c: 2-p : 2p 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P h on D h on D 2 h 2 on D 2 a P * P c +P Inro o ML p-a 6

17 c: 2-p : 2p 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P h on D h on D 2 h 2 on D 2 a ½-p+a Inro o ML ½ p-a 7

18 c: 2-p : 2p 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P 2-pa 2p½-p+a 2pp-a h on D h 2 on D 2 a ½-p+a Inro o ML ½ p-a 8

19 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P h on D 2-pa 2p½-p+a 2pp-a Error P +pp c +P c 3p 2-2p 3 Inro o ML 9

20 3 Wak Larnrs - Analyss Assum all WL ar p½ -γ P c P cc P c P h on D 2-pa 2p½-p+a 2pp-a Error P +pp c +P c 3p 2-2p 3 p3p-2p 2 p-4γ 2 Inro o ML 20

21 3 Wak Larnrs - Analyss Nw Error Old Error*-4γ 2-4γ 2 Inro o ML γ 2

22 Wha abou mor hypohss Th CS way Do rcursvly Can push down h rror arbrarly W show a mor consrucv way Inro o ML 22

23 ADABOOST: ADAPTIVE BOOSTING Inro o ML 23

24 AdaBoos: Ovrvw Buld a lnar classfr basc lmns, wak larnrs An onln approach ach m add on mor classfr F h sampl S Each m sp hav a hypohss f Slc a dsrbuon D on S Fnd a wak larnr h w.r. D Add h o h hypohss dcd on wgh a f + f +a h prdc sgnf + Inro o ML 24

25 AdaBoos: Algorhm Wak Larnrs H h: X{+,-} Inalzaon: FIXED Sampl S{,y..., m,y m } D /m Prdcon fsgnσα h Sp,, T D Inro o ML Rcv h WL w.r.. D Dfn ε Pr[h c*] α ½ log - ε / ε Dfn D +, Z D Z D - - y h y y h 26 h

26 AdaBoos: Inuon How do w chang h dsrbuon? Error wgh ncrass Corrc wgh dcrass Focus on h hard ampls Wha ar h paramrs? Th wak larnng class H Th numbr of raons T Assum o b npus Inro o ML 27

27 Illusrang AdaBoos Inal wghs for ach daa pon Orgnal Daa B Boosng Round

28 Boosng Illusrang AdaBoos B Round Boosng B Round B Boosng Round Ovrall

29 AdaBoos: Analyss Thorm: Gvn,..., T h ranng rror of f s boundd by T T 2 - Proof basd on hr clams Inro o ML 30

30 AdaBoos: Analyss Clam : whr fσα h Corollary : Proof For + w hav unravl rcurrnc - f y T Z D D h y Z D D f y h y T h y T Z D Z D Z D D T - T f y Z md 3 m D / snc Inro o ML

31 AdaBoos: Analyss Clam 2: Proof T Z S f rror, - m T T T m f y m m Z D Z Z md m m f y I m f sgn y I m S f rror 0, Corollary z<0 -z > 32 Inro o ML

32 AdaBoos: Analyss Clam 3: Proof 2 Z - D D D Z h y h y h y m : : rcall : h y D 33 α ½ log - ε / ε and subsu Inro o ML α ε ε

33 AdaBoos: Analyss Why hs valu of alpha? vald for any α Mnmz Z o rduc rror clam 2 2 ln 2 2 Z d dz Z - - Inro o ML

34 AdaBoos: Analyss Thorm: Gvn,..., T h rror of f s boundd by T T T g g 35 T Z Inro o ML

35 AdaBoos: Analyss Thorm: Gvn,..., T h rror of f s boundd by For 2 T T g > - g 2Tg - 2 T Inro o ML T - 4g 2 Z -2 g 2 36

36 Ensmbl Mhods

37 Ensmbl Mhods Hgh lvl da Gnra mulpl hypohss Combn hm o a sngl classfr Two mporan qusons How do w gnra mulpl hypohss w hav only on sampl How do w combn h mulpl hypohss Majory, AdaBoos, Inro o ML 39

38 Raonal for Ensmbl Mhods Sascal Compuaonal h f h h h h h f h h h f h Rprsnaonal Inro o ML 40 Sourc: hp://wb.ngr.orgonsa.du/~gd/publcaons/mcs-nsmbls.pdf

39 Boosng Boosng s acually an nsmbl mhod Gnrang dffrn hypohss: By changng h sampl dsrbuon Combnng hypohss wghd lnar prdcor Wghs drmn whn hypo. s slcd. Inro o ML 4

40 Baggng Inpu: a sngl larnng algorhm A How do w gnra dffrn Hypohss samplng wh rplacmn manans h sascs Formally, gvn a sampl S Sub sampl S,, S k Run A on S o gnra h Combnng: Smpl majory Inro o ML 42

41 Bas vs. Varanc Man Squar Error MSE E h [ f-h 2 ] Bas 2 + Var Whr Bas E[f-h] Var E[h 2 ] E 2 [h] MSE var bas 2 Inro o ML 43

42 Baggng raonal: Bas vrsus Var Why s on hypohss wors han many?! Epcd rror of h dncal o all h wors han ranng on all sampl smallr sampl BIAS Varanc of h rror sngl hypohss - flucuas consdrably majory of many - much mor sabl Mor sabl br gnralzaon h ranng rror br rflcs h ru rror Inro o ML 44

43 Dcson Tr vs. Baggng Sourc: hp:// Inro o ML 45

44 Sackng Inpu: Sampl S k algorhms A combng algo C Run A on S gnra h Gvn h,, h k gnra nw sampl,y h,,h k,y Run C o gnra ħ Oupu ħ Wha can b A? Wha can b C? Baggng: A sub-sampls C s a majory AdaBoos: A wak hypo m C wghd majory Inro o ML 46

45 Random Fors: movaon Dcson Trs: Bas Dcson r craon s vry nosy Dpnds on parcular sampl Lowrng Varanc: Avragng ovr dcson rs How can w gnra dffrn dcson rs? Sub-sampl h sampl Forc cran arbus Inro o ML 47

46 Random Fors: Algorhm Cra K dffrn dcson rs: Sampl: Slc a random sub-sampl Pracc: 66% GOAL: Gnra a vary of DT Wll corrlad wh y Combnng: Majory Arbus: In ach nod slc subs F of arbus F M Wak larnrs Slc h bs ar. n F Valus of M: M: random MN all arbus rgular DT << M<< N Subs of arbus Popular: M N Inro o ML 48

47 Random Fors Sourc: hp://cs.sanford.du/popl/karpahy/randomforsspral.png Inro o ML 49

48 Random Fors: Concluson Bnfs: Fas o run Farly sabl oucom Compv prformanc Handls mssng/paral daa Waknsss: Losss nrprably unlk DT Many paramrs Bu sms robus Faur slcon Collds wh arbus samplng Inro o ML 50

49 MNIST: Comparav rsuls Classfr Accuracy Tranng Tm Tsng Tm Nural N 97.80% s 0.398s Lnar SVM 94.6% s 58.00s Random Fors 96.4% 2.359s s k nars nghbors k % s s Dcson Tr dph % 3.346s 0.033s Adaboos 73.67% s.585s Sourc: hps://marn-homa.com/comparng-classfrs/ Inro o ML 5

50 Concluson Boosng Wak vs srong larnng Thory 3 o Pracc AdaBoos Ensmbl Mhods Boosng Baggng Sackng Random Fors Inro o ML 52

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