证明了ROC曲线下面积的概率意义，并给出不满足条件时的误差边界。
A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It
- 严格证明曲线下面积等于正负样本排序正确的概率
- 推导出假设不成立时该概率估计的最大偏差
- 适合机器学习评估、统计检验方向研究者阅读
二分类器的受试者工作特征（ROC）曲线常用于评估性能，其曲线下面积（AUC）因被解释为分类器将随机正样本排在随机负样本之前的概率而广受青睐。本文正式证明了这一概率解释的正确性，推导出当基本假设不满足时该估计值与真实值之间的最大偏差，并简要综述了ROC曲线的相关文献。
原文摘要 · Abstract (English)
The Receiver Operating Characteristic (ROC) curve of a binary classifier has often been utilized to measure the performance of the classifier. The area beneath this curve is used in particular because of its quoted probabilistic interpretation as being equal to the probability that the classifier will rank a random positive observation above a random negative observation. This paper formalizes this claim, produces a bound on how far away from the truth it is if a hypothesis is not met, and gives a small literature review of the ROC curve.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。