Transcription of Classification and Regression by randomForest
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,December200218 ClassificationandRegressionbyrandomFores tAndyLiawandMatthewWienerIntroductionRec entlytherehasbeenalotofinterestin ensem-blelearning (see, ,Shapireetal.,1998)andbaggingBreiman(199 6) , , ,successivetreesdonotdependonearliertree s , (2001)proposedrandomforests, , , , ,includingdiscriminantanalysis,supportve ctorma-chinesandneuralnetworks,andisrobu stagainstoverfitting(Breiman,2001).Inadd ition,itisveryuser-friendlyinthesensetha tithasonlytwoparam-eters(thenumberofvari ablesintherandomsubsetateachnodeandthenu mberoftreesintheforest), ( ). (forbothclassificationandregression) ,growanun-prunedclassificationorregressi ontree,withthefollowingmodification:atea chnode,ratherthanchoosingthebestsplitamo ngallpredic-tors,randomlysamplemtryofthe predictorsandchoosethebestsplitfromamong thosevariables.(Baggingcanbethoughtofast hespecialcaseofrandomforestsobtainedwhen mtry=p,thenumberofpredictors.)
Variable importance This is a difficult concept to define in general, because the importance of a variable may be due to its (possibly complex) ... to simulate the “class 2” data: 1. The “class 2” data are sampled from the prod-uct of the marginal distributions of the vari-
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