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).
A learning curve analysis shows that Iso-tonic Regression is more prone to overfitting, and thus per-forms worse than Platt Scaling, when data is scarce. ... P1 16 4000 14000 3% LETTER.P2 16 4000 14000 53% MEDIS 63 4000 8199 11% MG 124 4000 12807 17% SLAC 59 4000 25000 50% HS 200 4000 4366 24%.
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