In this note, I study how the precision of a binary 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 $r$.

中文翻译：

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类别不平衡对 Precision-Recall 曲线的影响

在这篇笔记中，我研究了二元分类器的精度如何取决于比率 $r$测试集中正面到负面案例的数量，以及分类器的真假阳性率。这种关系允许预测精确召回曲线将如何随$r$，这似乎并不为人所知。它还允许预测如何$F_{\beta }$ Flach 和 Kull (2015) 的精度增益和召回增益度量随 $r$.