Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predictions. While there are many algorithms that explain models, generating explanations in a format that is comprehensible and useful to decision-makers is a nontrivial task that can require extensive development overhead. We developed Pyreal, a highly extensible system with a corresponding Python implementation for generating a variety of interpretable ML explanations. Pyreal converts data and explanations between the feature spaces expected by the model, relevant explanation algorithms, and human users, allowing users to generate interpretable explanations in a low-code manner. Our studies demonstrate that Pyreal generates more useful explanations than existing systems while remaining both easy-to-use and efficient.

翻译：许多领域的用户借助机器学习（ML）预测辅助决策。有效的基于机器学习的决策通常需要理解ML模型及其预测的解释。尽管已有多种算法可解释模型，但以决策者易于理解且有用的形式生成解释仍是一项颇具挑战的任务，往往需要大量的开发工作。我们开发了Pyreal——一个高度可扩展的系统及相应的Python实现，用于生成各类可解释的机器学习解释。Pyreal可在模型所需的特征空间、相关解释算法及人类用户之间转换数据与解释，使用户能够以低代码方式生成可解释性解释。研究表明，相较于现有系统，Pyreal能生成更有用的解释，同时保持易用性与高效性。