title: Revisiting the area under the ROC.
abstract: The Receiver-Operating Characteristic curve or ROC has been a long standing and well appreciated tool to assess performance of classifiers or diagnostic tests. Likewise, the Area Under the ROC (AUC) has been a metric to summarize the power of a test or ability of a classifier in one measurement. This article aims to revisit the AUC, and ties it to key characteristics of the noncentral hypergeometric distribution. It is demonstrated that this statistical distribution can be used in modeling the behaviour of classifiers, which is of value for comparing classifiers.
authors: National Research Council, Institute for Information Technology, Ottawa, ON, Canada.
authors: Berry
authors: B
authors: De Bruijn
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003627:Data Interpretation, Statistical; D050723:Fractures, Bone / Q000175:diagnosis / Q000000981:diagnostic imaging; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D011871:Radiology / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic / Q000379:methods*