The Effect of Class Imbalance on Precision-Recall Curves

Abstract

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 $F_{\beta }$ and the Precision Gain and Recall Gain measures of Flach and Kull (2015) vary with $r$.

4 pages, 1 figure. Added ref to Siblini et al (2020) and last sentence. Final m/s version of paper published in Neural Computation

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