Transcription of Predicting Good Probabilities With Supervised Learning
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Predicting Good Probabilities With Supervised Learning
IthacaNY14853 AbstractWe showthatmaxi-mummarginmethodssuchasboost edtreesandboostedstumpspushprobabilityma ssawayfrom0 and1 yieldinga Bayes,whichmake unrealis-ticindependenceassumptions,push probabilitiestoward0 ex-perimentwithtwo waysofcorrectingthebiasedprobabilitiespr edictedbysomelearningmeth- muchdatathey ,randomforests, IntroductionInmany applicationsit isimportanttopredictwellcali-bratedproba bilities;goodaccuracy orareaundertheROCcurve :SVMs,neuralnets,decisiontrees,memory-ba sedlearn-ing,baggedtrees,randomforests,b oostedtrees,boostedstumps,naive show howmaximummarginmethodssuchasSVMs,booste dtrees,andboostedstumpstendtopushpredict edprobabilitiesawayfrom0 predictandyieldsa bayeshave theoppositebiasandtendtopushpredictionsc loserto0 , Bonn,Germany, (s)/owner(s).
Set m^k;l = (wk;i 1m^k;i 1 +wi;lm^i;l)=wk;l Replace m^k;i 1 and m^i;l with m^k;l 4 Output the stepwise const. function: m^(f) = m^i;j, for fi < f fj where m is an isotonic (monotonically increasing) func-tion. Then, given a train set (fi;yi), the Isotonic Regres-sion problem is finding the isotonic function m^ such that
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