Transcription of Classification and Regression by randomForest
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Classification and Regression by randomForest
,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.) ( ,majorityvotesforclassification,averagef orregression).Anestimateoftheerrorrateca nbeobtained,basedonthetrainingdata, ,predictthedatanotinthebootstrapsample(w hatBreimancalls out-of-bag ,orOOB,data) (Ontheav-erage,eachdatapointwouldbeout-o f-bagaround36%ofthetimes,soaggregatethes epredictions.)
randomForest performs unsupervised learning (see below). Currently randomForest does not handle ordinal categorical responses. Note that categorical predictor variables must also be specified as factors (or else they will be wrongly treated as continuous). The randomForest function returns an object of class "randomForest". Details on the ...
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