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.

翻译：不确定性的定量表征与估计在优化和决策过程中具有根本重要性。本文提出了直觉性评分指标——我们称之为确定性与怀疑度——这些指标可同时应用于贝叶斯和频率学派框架，用于评估和比较（多）分类决策机器学习问题中预测的质量与不确定性。