# Measuring Classification Decision Certainty and Doubt

arXiv: https://arxiv.org/abs/2303.14568 · HTML: https://mlsanity.com/paper/2303.14568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Evaluating model calibration in classification](https://mlsanity.com/paper/1902.06977/) (2019)

## Abstract (arXiv)

Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, which we call certainty and doubt, that can be used in both a Bayesian and frequentist framework to assess and compare the quality and uncertainty of predictions in (multi-)classification decision machine learning problems.