Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visual factors we believe have a positive impact on rule readability and understanding. The paper presents an initial design space for visualizing rule sets and a user study exploring their impact. The results show that some design factors have a strong impact on how efficiently readers can process the rules while having minimal impact on accuracy. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.

中文翻译：

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可视化规则集：设计空间的探索和验证

规则集在机器学习（ML）中经常用作在需要透明性和可理解性的环境中传达模型逻辑的一种方式。规则集通常表示为基于文本的逻辑语句（规则）列表。令人惊讶的是，迄今为止，在探索视觉呈现方式以替代规则方面的工作还很有限。在本文中，我们探索设计规则的替代表示的想法，重点关注一些我们认为会对规则的可读性和理解产生积极影响的视觉因素。本文介绍了用于可视化规则集的初始设计空间，以及用于研究其影响的用户研究。结果表明，一些设计因素对读者处理规则的效率有很大影响，而对准确性的影响却很小。