Receiver operating characteristic (ROC) curves are commonly used to evaluate predictions of binary outcomes. When there is a small percentage of items of interest (as would be the case with fraud detection, for example), ROC curves can provide an inflated view of performance. This can cause challenges in determining which set of predictions is better. In this article, we discuss the conditions under which precision-recall curves may be preferable to ROC curves. As an illustrative example, we compare two commonly used fraud predictors (Beneish’s [1999, Financial Analysts Journal 55: 24–36] M score and Dechow et al.’s [2011, Contemporary Accounting Research 28: 17–82] F score) using both ROC and precision-recall curves. To aid the reader with using precision-recall curves, we also introduce the command prcurve to plot them.

接收器工作特性（ROC）曲线通常用于评估二进制结果的预测。当感兴趣的项目所占比例很小时（例如，欺诈检测就是这种情况），ROC曲线可以提供出色的绩效视图。这可能会在确定哪个预测集更好时带来挑战。在本文中，我们讨论了精确调用曲线可能优于ROC曲线的条件。作为说明性示例，我们比较了两个常用的欺诈预测因子（Beneish [1999，Financial Analysts Journal 55：24-36] M得分和Dechow等人[2011，当代会计研究28：17-82] F分数），同时使用ROC和精确调用曲线。为了帮助读者使用精确调用曲线，我们还引入了prcurve命令来绘制它们。