AI RESEARCH
Truthful Calibration Errors for Multi-Class Prediction
arXiv CS.LG
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ArXi:2510.06388v2 Announce Type: replace Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune probabilistic predictors. Recently, Haghtalab We study the practical role of truthfulness for calibration measurement in multiclass prediction. First, we