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Period ending 2026-09-14

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A Ranking Approach for Measuring Calibration
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Sep 14, 2026 · stat.MEJ/K move · Enter open · S save

A Ranking Approach for Measuring Calibration

Anirban Chatterjee, Rina Foygel Barber

When providing forecasted probabilities with a predictive model, the ideal model offers perfect calibration: the true probability of the outcome (i.e., the probability that

Y=1

) exactly matches the forecasted probability

f(X)

. In practice, models inevitably exhibit calibration error, and it is therefore important to be able to measure this miscalibration to assess a model's reliability. The Expected Calibration Error (ECE) is the most widely used measure of miscalibration, but is known to be impossible to estimate the ECE with guaranteed accuracy in an assumption-free setting. In this work, we propose an alternative measure, the rankECE, that is based on comparing points with neighboring values of the predicted probability

f(X)

. Our theoretical guarantees and empirical results establish that rankECE provides a better proxy for ECE as compared to binned approximations to ECE, which are the most commonly-used approximations in practice.

Probability Generalizable Ranking Purpose

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