School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K.
Neural Comput. 2021 Apr 1;33(4):853-857. doi: 10.1162/neco_a_01362.
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 Fβ and the precision gain and recall gain measures of Flach and Kull (2015) vary with r.
在本说明中，我研究了二分类器的精度如何取决于测试集中正例与负例的比例 r，以及分类器的真阳性率和假阳性率。这种关系可以预测精度-召回曲线随 r 的变化情况，而这似乎尚未广为人知。它还可以预测 Flach 和 Kull（2015 年）的 Fβ 和精度增益、召回增益度量如何随 r 变化。
