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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).

Type of random forest: regression Number of trees: 500 No. of variables tried at each split: 4 Mean of squared residuals: 10.64615 %Varexplained:87.39 The “mean of squared residuals” is computed as MSE OOB = n−1 n ∑ 1 {y i − yˆOOB i} 2, where yˆOOB i is the average of the OOB predictions for the ith observation. The “percent ...

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