An introduction to ROC analysis - Stanford University

An introduction to ROC analysisTom FawcettInstitute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USAA vailable online 19 December 2005 AbstractReceiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphsare commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data miningresearch. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research. 2005 Elsevier All rights :ROC analysis ; Classifier evaluation; Evaluation metrics1. IntroductionA receiver operating characteristics (ROC) graph is atechnique for visualizing, organizing and selecting classifi-ers based on their performance. ROC graphs have longbeen used in signal detection theory to depict the tradeoffbetween hit rates and false alarm rates of classifiers (Egan,1975; Swets et al.)

An introduction to ROC analysis Tom Fawcett Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA Available online 19 December 2005 Abstract ... sensitivity ¼recall specificity ¼ ...

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