V-ECE: Estimating General Expected Calibration Errors

תקציר מקורי באנגליתarXiv:2602.24230v2 Announce Type: replace-cross Abstract: In probabilistic classification, calibration error (CE) measures the average divergence of predicted probabilities $f(X)$ from $\mathbb{P}(Y|f(X))$, the true class distribution for that predicted probability. While being a useful diagnostic tool, it is hard to estimate: popular binning-based estimators are often inconsistent and scale poorly beyond two classes. Recent work rewrites the CE as the excess risk of a model compared to the best recalibration of its own predictions, measured with a proper loss. However, this only works for Bregman-divergence-based calibration errors like the squared error, excluding the more popular $L_1$-distance-based CE. We show that using prediction-dependent proper scores can alleviate this restrictio