# Training with very few positives

I have a binary classification problem where the fraction of positives is very low, e.g. 20 positives in 10,000 examples (0.2%)

What is an appropriate cross validation scheme for training a classifier with very few positives?

I currently have the following setup:

library(caret)
tmp <- createDataPartition(Y, p = 9/10, times = 3, list = TRUE)
myCtrl <- trainControl(method = "boot", index = tmp, timingSamps = 2, classProbs = TRUE, summaryFunction = twoClassSummary)

RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1, metric="ROC")