With anomaly detection, if you have labeled outliers there are two types of models. The first one requires the outliers, although regularly unlabeled. Isolation forests fall in this category. One class SVM specifically works better without the outliers in there. Properly evaluating this model does require the outliers though. The current sklearn setup does not allow for this case (I believe). It would be nice to have a way to do this easily.

One possible approach would be to use a different type of validation iterator, that returns only negative sample indices in the training fold but both in the validation fold.