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No, the point is for an imbalanced set you literally don't care about the model performance where the false-positive-rate is substantial. IE, let's say you have
by eanzenberg 9y ago
No, the point is for an imbalanced set you literally don't care about the model performance where the false-positive-rate is substantial. IE, let's say you have 1% true-hits in your data and run the classifier at an FPR of 5%, that means you are generating ~5 false-positives which is insane to do!
That's why most of the ROC curve is useless for imbalanced sets. That's why I prefer precision/recall graphs as does the OP.