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).
Vol. 2/3, December 2002 20 Measure 1 RI Mg Ca Ba Si Al Fe 010 20 30 40 K Na Measure 2 RI Mg Al Ca Ba K Na Si 05 10 15 Fe Measure 3 RI Mg Ca Al Ba 0.0 0.2 0.4 0.6 K Si Na Fe
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