With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common goal in uncertainty quantification (UQ) in machine learning is to approximate the true distribution of the target data, many works in UQ tend to be disjoint in the evaluation metrics utilized, and disparate implementations for each metric lead to numerical results that are not directly comparable across different works. To address this, we introduce Uncertainty Toolbox, an open-source python library that helps to assess, visualize, and improve UQ. Uncertainty Toolbox additionally provides pedagogical resources, such as a glossary of key terms and an organized collection of key paper references. We hope that this toolbox is useful for accelerating and uniting research efforts in uncertainty in machine learning.

本文主要介绍了一种名为Uncertainty Toolbox的开源Python 工具库，该库旨在协助评估、可视化和改进在机器学习中的不确定性计量（UQ）。 Uncertainty Toolbox不仅提供教学资源，例如重点词汇表和重要论文引用的组织集合，也有助于加速和整合应用在机器学习中的不确定性计量研究。

不确定性工具箱：一个用于评估、可视化和改善不确定性量化的开源库