arXiv Machine Learning
Sep 14

A Ranking Approach for Measuring Calibration

The paper introduces rankECE, a new metric for assessing calibration error in predictive models. Unlike the widely used Expected Calibration Error (ECE), rankECE compares predictions with neighboring probability values, offering theoretical guarantees and empirical evidence that it better approximates ECE than traditional binned methods.

By Anirban Chatterjee, Rina Foygel Barber