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When calculating AUC of ROC, most people use false positve and true positve as their axises. From my understanding, efficiency corresponds to true positve but purity does not match to 1 - false positive.
purity = true signals that passed the cut / events that passed the cut
1 - false positive = true backgrounds that failed the cut / true backgrounds
The consequence is that the ROC curve of efficiency and purity does not start and end at the diagonal points. Is my understanding correct?
The text was updated successfully, but these errors were encountered:
OK I figured out you were using the integral of precision-recall curve. It would be nice if you can point this out in the comment of the source code. However, since precision-recall curve is more sensitive to imbalanced data, is there any particular reason not to use the AUC of false positive vs true positive?
When calculating AUC of ROC, most people use false positve and true positve as their axises. From my understanding, efficiency corresponds to true positve but purity does not match to 1 - false positive.
The consequence is that the ROC curve of efficiency and purity does not start and end at the diagonal points. Is my understanding correct?
The text was updated successfully, but these errors were encountered: