Making and Evaluating Calibrated Forecasts

• URL: http://arxiv.org/abs/2510.06388v1
• Date: Tue, 07 Oct 2025 19:11:03 GMT
• Title: Making and Evaluating Calibrated Forecasts
• Authors: Yuxuan Lu, Yifan Wu, Jason Hartline, Lunjia Hu,
• Abstract summary: We introduce a perfectly truthful calibration measure for multi-class prediction tasks.<n>We mathematically prove and empirically verify that our calibration measure exhibits superior robustness.<n>This result addresses the non-robustness issue of binned ECE.
• Score: 10.153382419318023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Calibrated predictions can be reliably interpreted as probabilities. An important step towards achieving better calibration is to design an appropriate calibration measure to meaningfully assess the miscalibration level of a predictor. A recent line of work initiated by Haghtalab et al. [2024] studies the design of truthful calibration measures: a truthful measure is minimized when a predictor outputs the true probabilities, whereas a non-truthful measure incentivizes the predictor to lie so as to appear more calibrated. All previous calibration measures were non-truthful until Hartline et al. [2025] introduced the first perfectly truthful calibration measures for binary prediction tasks in the batch setting. We introduce a perfectly truthful calibration measure for multi-class prediction tasks, generalizing the work of Hartline et al. [2025] beyond binary prediction. We study common methods of extending calibration measures from binary to multi-class prediction and identify ones that do or do not preserve truthfulness. In addition to truthfulness, we mathematically prove and empirically verify that our calibration measure exhibits superior robustness: it robustly preserves the ordering between dominant and dominated predictors, regardless of the choice of hyperparameters (bin sizes). This result addresses the non-robustness issue of binned ECE, which has been observed repeatedly in prior work.

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