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：不确定性量化和估计在优化和决策过程中具有基本重要性。在本论文中，我们提出了直观的评分，称为确定度和疑虑度，它们可以用贝叶斯和频率学派框架中用于评估和比较( 多)分类决策机器学习问题中的预测质量和不确定性。