Attributing AUC-ROC to Analyze Binary Classifier Performance. (arXiv:2205.11781v1 [cs.LG])

Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular
evaluation metric for binary classifiers. In this paper, we discuss techniques
to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an
additive/linear function over the data samples, therefore such segmenting the
overall AUC-ROC is different from tabulating the AUC-ROC of data segments. To
segment the overall AUC-ROC, we must first solve an \emph{attribution} problem
to identify credit for individual examples.

We observe that AUC-ROC, though non-linear over examples, …

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