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

arXiv: https://arxiv.org/abs/2109.09160 · HTML: https://mlsanity.com/paper/2109.09160/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 8 of 8)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [An Evaluation of the Human-Interpretability of Explanation](https://mlsanity.com/paper/1902.00006/) (2019)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [Falling Rule Lists](https://mlsanity.com/paper/1411.5899/) (2014)

## Built on by (most cited first, 5 of 5)

- [VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees](https://mlsanity.com/paper/2112.00334/) (2021)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)
- [RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers](https://mlsanity.com/paper/2409.03164/) (2024)
- [DASS Good: Explainable Data Mining of Spatial Cohort Data](https://mlsanity.com/paper/2304.04870/) (2023)

## Abstract (arXiv)

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.