Transcription of Predicting Good Probabilities With Supervised Learning
{{id}} {{{paragraph}}}
Example: biology
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).
Predicting Good Probabilities With Supervised Learning also justified for boosted trees and boosted stumps. Let the output of a learning method be f(x).
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Understanding, Predicting, & Reducing Appointment, Predicting, For Predicting Outcomes of Basketball, CHM 130 Predicting Products Worksheet, The best features for predicting stock, Reactions, Northern California Training Academy, Predicting the Future, Predicting Recessions with Leading Indicators: Model, Predicting Outcomes of Case-based Legal Arguments