Measuring classifier performance: a coherent alternative to the area under the ROC curve
 David J. Hand
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Abstract
The area under the ROC curve (AUC) is a very widely used measure of performance for classification and diagnostic rules. It has the appealing property of being objective, requiring no subjective input from the user. On the other hand, the AUC has disadvantages, some of which are well known. For example, the AUC can give potentially misleading results if ROC curves cross. However, the AUC also has a much more serious deficiency, and one which appears not to have been previously recognised. This is that it is fundamentally incoherent in terms of misclassification costs: the AUC uses different misclassification cost distributions for different classifiers. This means that using the AUC is equivalent to using different metrics to evaluate different classification rules. It is equivalent to saying that, using one classifier, misclassifying a class 1 point is p times as serious as misclassifying a class 0 point, but, using another classifier, misclassifying a class 1 point is P times as serious, where p≠P. This is nonsensical because the relative severities of different kinds of misclassifications of individual points is a property of the problem, not the classifiers which happen to have been chosen. This property is explored in detail, and a simple valid alternative to the AUC is proposed.
 Title
 Measuring classifier performance: a coherent alternative to the area under the ROC curve
 Journal

Machine Learning
Volume 77, Issue 1 , pp 103123
 Cover Date
 200910
 DOI
 10.1007/s1099400951195
 Print ISSN
 08856125
 Online ISSN
 15730565
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 ROC curves
 Classification
 AUC
 Specificity
 Sensitivity
 Misclassification rate
 Cost
 Loss
 Error rate
 Industry Sectors
 Authors

 David J. Hand ^{(1)} ^{(2)}
 Author Affiliations

 1. Department of Mathematics, Imperial College London, London, UK
 2. Institute for Mathematical Sciences, Imperial College London, London, UK