XiaoLiu, I can't see the options in bootControl you used here. Your error is consistent with leaving classProbs and summaryFunction unspecified. Please double check that you set them with classProbs = TRUE and summaryFunction = twoClassSummary before you ran.
Max On Thu, May 12, 2011 at 7:04 PM, Jing Liu <quiet_jing0...@hotmail.com> wrote: > > 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 > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Max ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.