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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