Here is a snippet to show what i'm trying to do.
library(randomForest)
library(ROCR)
library(caret)
data(iris)
iris <- iris[(iris$Species != "setosa"),]
fit <- randomForest(factor(Species) ~ ., data=iris, ntree=50)
train.predict <- predict(fit,iris,type="prob")[,2]
plot(performance(prediction(train.predict,factor(iris$Species)),"tpr","fpr"),col
= "red")
#As expected AUC is 1 because we are using the same dataset to validate
auc1 <-
performance(prediction(train.predict,factor(iris$Species)),"auc")@y.values[[1]]
legend("bottomright",legend=c(paste("Random Forests
(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
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