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September 29, 2025Open Access

Recalibrating binary probabilistic classifiers

Key Points

The proposed recalibration methods improve performance under distribution shift, which is crucial for credit risk management.
New methods, parametric covariate shift with posterior drift and ROC-based quasi moment matching, show promise in recalibration tasks.
Testing of these methods indicates they yield conservative results, especially when evaluating risk weight functions.
Effective recalibration of classifiers can enhance decision-making in financial sectors, particularly in assessing credit risk.

Abstract

Recalibration of binary probabilistic classifiers to a target prior probability is an important task in areas like credit risk management. We analyse methods for recalibration from a distribution shift perspective. Distribution shift assumptions linked to the area under the curve (AUC) of a probabilistic classifier are found to be useful for the design of meaningful recalibration methods. Two new methods called parametric covariate shift with posterior drift (CSPD) and ROC-based quasi moment matching (QMM) are proposed and tested together with some other methods in an example setting. The outcomes of the test suggest that the QMM methods discussed in the paper can provide appropriately conservative results in evaluations with concave functionals like for instance risk weights functions for credit risk.

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