"What happens when X-validation creates partition with no positive examples?"
If a dataset is skewed, then positive and negative example sets are not be balanced in size. Skew is common when trying to learn a detector of rare events, for example.
Suppose that the data set has only 1 positive example. Then cross validation can produce only one training subset that has a positive example; the others will have no positive examples. What will happen? What do RapidMiner Models do when trained without a positive example?
I'm asking because I'm seeing a Java Exception in model training that I've traced back to an XVal partition with no positive examples.
Is there a way to detect the situation and skip training in this case?