Scalable Utility-Aware Multiclass Calibration

• URL: http://arxiv.org/abs/2510.25458v1
• Date: Wed, 29 Oct 2025 12:32:14 GMT
• Title: Scalable Utility-Aware Multiclass Calibration
• Authors: Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut,
• Abstract summary: Utility calibration is a general framework that measures the calibration error relative to a specific utility function.<n>We demonstrate how this framework can unify and re-interpret several existing calibration metrics.
• Score: 53.28176049547449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring that classifiers are well-calibrated, i.e., their predictions align with observed frequencies, is a minimal and fundamental requirement for classifiers to be viewed as trustworthy. Existing methods for assessing multiclass calibration often focus on specific aspects associated with prediction (e.g., top-class confidence, class-wise calibration) or utilize computationally challenging variational formulations. In this work, we study scalable \emph{evaluation} of multiclass calibration. To this end, we propose utility calibration, a general framework that measures the calibration error relative to a specific utility function that encapsulates the goals or decision criteria relevant to the end user. We demonstrate how this framework can unify and re-interpret several existing calibration metrics, particularly allowing for more robust versions of the top-class and class-wise calibration metrics, and, going beyond such binarized approaches, toward assessing calibration for richer classes of downstream utilities.

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