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A Ranking Approach for Measuring Calibration
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T17:34:25.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.