论文标题
解释性和解释性：机器学习动物园迷你旅行
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
论文作者
论文摘要
在这篇评论中，我们研究了设计可解释和可解释的机器学习模型的问题。可解释性和解释性在于许多机器学习和医学，经济学，法律和自然科学中的统计应用的核心。尽管可解释性和解释性逃脱了一个明确的普遍定义，但是这些特性动机的许多技术是在最近30年中开发的，目前将重点转移到深度学习方法上。在这篇综述中，我们强调了可解释性和解释性之间的鸿沟，并用最先进的具体例子说明了这两个不同的研究方向。该评论旨在为一般的机器学习受众介绍，对探索逻辑回归或随机森林可变重要性的解释和解释问题的兴趣。这项工作不是详尽的文献调查，而是底漆，有选择地关注作者发现有趣或有益的研究。
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have escaped a clear universal definition, many techniques motivated by these properties have been developed over the recent 30 years with the focus currently shifting towards deep learning methods. In this review, we emphasise the divide between interpretability and explainability and illustrate these two different research directions with concrete examples of the state-of-the-art. The review is intended for a general machine learning audience with interest in exploring the problems of interpretation and explanation beyond logistic regression or random forest variable importance. This work is not an exhaustive literature survey, but rather a primer focusing selectively on certain lines of research which the authors found interesting or informative.
