Measuring Uncertainty Calibration

ICLR 2026 Conference Submission19283 Authors

Keywords: calibration, classification

TL;DR: We describe principled ways of bounding the calibration error of a classifier.

Abstract: We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error can be upper bounded efficiently without significantly impacting classifier performance and without any restrictive assumptions. All our results are non-asymptotic and distribution-free. We conclude by providing advice on how to measure calibration error in practice. Our methods yield practical procedures that can be run on real-world datasets with modest overhead.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 19283