PAV and the ROC convex hull

Authors:
Tom Fawcett;Alexandru Niculescu-Mizil
Affiliations:
Center for the Study of Language and Information, Stanford University, Stanford, USA 94305;Computer Science Department, Cornell University, Ithaca, USA 14853
Venue:
Machine Learning
Year:
2007

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Abstract

Classifier calibration is the process of converting classifier scores into reliable probability estimates. Recently, a calibration technique based on isotonic regression has gained attention within machine learning as a flexible and effective way to calibrate classifiers. We show that, surprisingly, isotonic regression based calibration using the Pool Adjacent Violators algorithm is equivalent to the ROC convex hull method.