consider higher confidence class probability
Binary classification cross validation ROC score - only consider higher confidence class probabilities
I had no success using regression, so first I'll use classification to determine which samples are zero, then do regression on the rest. The regression approach works quite well when there aren't a ton of zero values in y) Is this a valid approach to improving the ROC score? I can't see any reason why not but ML is not my specialty and I might be missing something. If it is valid, do I have to watch out for any class imbalances in the resulting high confidence test set when computing the ROC score?