The Effect of Class Imbalance on Precision-Recall Curves
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In this note I study how the precision of a classifier depends on the ratio r of positive to negative cases in the test set, as well as the classifier's true and false positive rates. This relationship allows prediction of how the precision-recall curve will change with r, which seems not to be well known. It also allows prediction of how F_β and the Precision Gain and Recall Gain measures of Flach and Kull (2015) vary with r.
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