A Ranking Approach for Measuring Calibration

Abstract: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.

Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.13100 [stat.ME]

(or arXiv:2609.13100v1 [stat.ME] for this version)

https://doi.org/10.48550/arXiv.2609.13100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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