# Boldness-Recalibration for Binary Event Predictions

arXiv: https://arxiv.org/abs/2305.03780 · HTML: https://mlsanity.com/paper/2305.03780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 3 of 3)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited](https://mlsanity.com/paper/2008.03033/) (2020)
- [Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates](https://mlsanity.com/paper/2204.04674/) (2022)

## Built on by (most cited first, 1 of 1)

- [Monitoring the calibration of probability forecasts with an application to concept drift detection involving image classification](https://mlsanity.com/paper/2510.25573/) (2025)

## Abstract (arXiv)

Probability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., spread out enough to be informative for decision making. However, there is a fundamental tension between calibration and boldness, since calibration metrics can be high when predictions are overly cautious, i.e., non-bold. The purpose of this work is to develop a Bayesian model selection-based approach to assess calibration, and a strategy for boldness-recalibration that enables practitioners to responsibly embolden predictions subject to their required level of calibration. Specifically, we allow the user to pre-specify their desired posterior probability of calibration, then maximally embolden predictions subject to this constraint. We demonstrate the method with a case study on hockey home team win probabilities and then verify the performance of our procedures via simulation. We find that very slight relaxation of calibration probability (e.g., from 0.99 to 0.95) can often substantially embolden predictions when they are well calibrated and accurate (e.g., widening hockey predictions range from .26-.78 to .10-.91).