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by eanzenberg
3100 days ago
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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 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. |
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