评估机器学习中的解释能力:一个关键的评论

Maissae Haddouchi, A. Berrado
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引用次数: 5

摘要

机器学习(ML)方法和模型的可解释性是一个涉及广泛数据挖掘研究的基本问题。这个话题不仅是一个学术问题,也是公众在实际环境中接受机器学习的一个关键方面。事实上,人们应该知道,缺乏可解释性可能是各种应用领域的真正缺点,例如医疗保健、生物学、社会学和工业决策支持系统。事实上,如果一种算法不能提供足够的关于学习者过程和学习模型的信息,那么它就会被抛弃,转而采用更不准确、更可解释的方法。已经有几篇论文提出解释有效模型,如神经网络和随机森林,但对于可解释性指的是什么,仍然没有达成共识。有趣的是,根据每个作者的观点,以及所处理问题的性质和解释所涉及的用户,该术语与不同的概念联系在一起。因此,本文的主要目的是对文献报道的机器学习过程和结果模型的可解释性相关方面进行细致的概述,并将上述方面组织成可用于机器学习可解释性评分的指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing interpretation capacity in Machine Learning: A critical review
Interpretability of Machine Learning (ML) methods and models is a fundamental issue that concerns a wide range of data mining research. This topic is not only an academic concern, but a crucial aspect for public acceptance of ML in practical contexts as well. Indeed, one should know that the lack of interpretability can be a real drawback for various application areas, such as in healthcare, biology, sociology and industrial decision support systems. In fact, an algorithm, which does not give enough information about the learner process and the learned model would be merely discarded in favor of less accurate and more interpretable approaches. Several papers have been proposed to interpret efficient models, such as Neural Networks and Random Forest, but there is still no consensus about what interpretability refers to. Interestingly, the term has been associated with different notions depending on the point of view of each author, as well as the nature of the issue being treated and the users concerned by the explanation. Therefore, this paper primarily aims to provide a painstaking overview of the aspects related to interpretability of ML learning process and resulting models, as reported by the literature, and to organize the aforementioned aspects into metrics that can be used for ML Interpretability scoring.
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