I am currently working on an imbalanced data-set (1% of 1). However I am a bit concerned by the underlying model.
I treated the problem as a classification problem, making some hypotheses on the distinguishability of the classes, I have been using different set of classifier, with good AUC (up to 0.9).
But the theory suggest that each instance as a low probability of being 1 with each instance characteristic changing the probability (say from 0.1% to 10%). In other terms, I have a rare event model. In this approach an instance with output 0 would be nearly indistinguishable from an instance with output 1. I feel like regression techniques should be used.
Does a rare event model, instead of a rare class one, invalidate the classification approach ? Or the 0.9 AUC is good enough so that the classification approach should hold ?