No bins needed: new method estimates model calibration error as a drop in risk
BlackHC · x · 2026-10-27
Researchers propose an efficient way to estimate calibration errors without binning. The trick: write the calibration error as a drop in risk under the right loss function. This yields a lower bound on the calibration error and, for free, signals whether a model is over- or under-confident — avoiding the bias and hyperparameter choices that come with bin-based estimators. Work with Francis Bach et al.
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