Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection

Receiver Operating Characteristic (ROC) curves are useful for evaluating binary classification models, but difficult to use for learning since the Area Under the Curve (AUC) is a piecewise constant function of predicted values. ROC curves can also be used in other problems with false positive and true positive rates such as changepoint detection. We show that in this more general context, the ROC curve can have loops, points with highly sub-optimal error rates, and AUC greater than one. This observation …

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