This paper introduces the concept of resolving power to describe the capacity of an evaluation metric to discriminate between models of similar quality. This capacity depends on two attributes: 1. The metric's response to improvements in model quality (its signal), and 2. The metric's sampling variability (its noise). The paper defines resolving power as a metric's sampling uncertainty scaled by its signal. Resolving power's primary application is to compare the discriminating capacity of threshold-free evaluation metrics, such as the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). A simulation study compares the AUROC and the AUPRC in a variety of contexts. The analysis suggests that the AUROC generally has greater resolving power, but that the AUPRC is superior in some conditions, such as those where high-quality models are applied to low prevalence outcomes. The paper concludes by proposing an empirical method to estimate resolving power that can be applied to any dataset and any initial classification model.

翻译：本文介绍了一种名为“分辨能力”的概念，用以描述评估指标区分质量相近模型的能力。该能力取决于两个属性：1）指标对模型质量改进的响应（即其信号），2）指标的采样变异性（即其噪声）。本文将分辨能力定义为指标的采样不确定性按信号缩放后的结果。分辨能力的主要应用是用于比较无阈值评估指标（如受试者工作特征曲线下面积（AUROC）和精确率-召回率曲线下面积（AUPRC））的区分能力。一项模拟研究在多种情景下比较了AUROC和AUPRC。分析表明，AUROC通常具有更强的分辨能力，但在某些条件下（例如，当高质量模型应用于低患病率结局时），AUPRC更为优越。本文最后提出了一种可应用于任意数据集和任意初始分类模型的经验性方法，用于估计分辨能力。