An introduction to ROC analysis

被引：13459

作者：

Fawcett, Tom ^{[1
]}

机构：

[1] Inst Study Learning & Expertise, Palo Alto, CA 94306 USA

来源：

PATTERN RECOGNITION LETTERS | 2006年 / 27卷 / 08期

关键词：

ROC analysis; classifier evaluation; evaluation metrics;

D O I：

10.1016/j.patrec.2005.10.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：861 / 874

页数：14

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