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...rests (AUC=",formatC(auc1,digits=4,format="f"),")",sep="")), col=c("red"), lty=1) #Cross validation using 10 fold CV: ctrl <- trainControl(method = "cv", classProbs = TRUE, summaryFunction = twoClassSummary) set.seed(1) rfEstimate <- train(factor(Species) ~ .,data = iris, method = "rf", metric = "ROC", tuneGrid = data.frame(.mtry = 2), trControl = ctrl) rfEstimate How can i plot the results from the cross validation on the previous ROC plot ? thanks, david

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be