An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets

TL;DR

This paper investigates visual representations of rule sets in machine learning to improve readability and understanding, conducting a user study to evaluate the impact of various visual factors on rule comprehension.

Contribution

It introduces and empirically evaluates visual design factors for rule presentation, enhancing interpretability of rule-based ML models.

Findings

Certain visual factors significantly improve processing efficiency.

Minimal impact of visual factors on rule accuracy.

Guidelines for designing more effective rule visualizations.

Abstract

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. We then presents 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.