mlsanityData to 7 Oct 2026

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

arXiv 2109.09160 ↗First posted 19 Sep 2021cs.HC cs.AIcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 19 Sep 2021.

Builds on

8 of 32 references.

References · 8

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Learning Explanatory Rules from Noisy Datacited by 210
  3. 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
  4. 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
  5. 2019An Evaluation of the Human-Interpretability of Explanationcited by 71
  6. 2016Scalable Bayesian Rule Listscited by 64
  7. 2018RuleMatrix: Visualizing and Understanding Classifiers with Rulescited by 62
  8. 2014Falling Rule Listscited by 58

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.

5 papers.

  1. 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescited by 8
  2. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5
  3. 2023DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumpscited by 2
  4. 2024RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifierscited by 1
  5. 2023DASS Good: Explainable Data Mining of Spatial Cohort Datacited by 0