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 $r$.

翻译：在本说明中,我研究了一个分类器的精确度如何取决于测试集中正对负案例的正对负比率,以及分类器的真实和假正率,这种关系使人们可以预测精确召回曲线如何与美元变化,美元似乎并不广为人知,还可以预测F ⁇ beta}$和Flach和Kull(2015年)的精确增益和回收收益措施如何因美元而异。