
Dear all,
 
I am using the "caret" Package for predictors selection with a randomForest model. The following is the train function:
 
rfFit<- train(x=trainRatios, y=trainClass, method="rf", importance = TRUE, do.trace = 100, keep.inbag = TRUE, 
    tuneGrid = grid, trControl=bootControl, scale = TRUE, metric = "ROC")
 
I wanted to use ROC as the metric for variable selection. I know that this works with the logit model by making sure that classProbs = TRUE and summaryFunction = twoClassSummary in the trainControl function. However if I do the same with randomForest, I get a warning saying that 
 
"In train.default(x = trainPred, y = trainDep, method = "rf",  :
  The metric "ROC" was not in the result set. Accuracy will be used instead."
 
I wonder if ROC metric can be used for randomForest? Have I missed something? Very very grateful if anyone can help!
 
Best regards,
XiaoLiu

 
  		 	   		  
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