ROC Graphs: Notes and Practical Considerations for Researchers
ROC Graphs: Notes and Practical Considerations for Researchers
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发表时间：
2007
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通讯作者：
Tom Fawcett
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作者：
Tom Fawcett
受试者工作特征（ROC）图是一种有用的技术，用于组织分类器和可视化其性能。ROC图通常用于医疗决策，近年来越来越多地被机器学习和数据挖掘研究社区采用。虽然ROC图看起来很简单，但在实际使用中存在一些常见的误解和陷阱。这篇文章既是ROC图的教程介绍，也是在研究中使用它们的实用指南。
Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research.