# VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees

arXiv: https://arxiv.org/abs/2112.00334 · HTML: https://mlsanity.com/paper/2112.00334/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 140 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification](https://mlsanity.com/paper/1503.01161/) (2015)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models](https://mlsanity.com/paper/1808.00196/) (2018)
- [Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers](https://mlsanity.com/paper/1504.07676/) (2015)
- [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015)
- [Boxer: Interactive Comparison of Classifier Results](https://mlsanity.com/paper/2004.07964/) (2020)
- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [Visualizing Data using GTSNE](https://mlsanity.com/paper/2108.01301/) (2021)
- [Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles](https://mlsanity.com/paper/2005.04289/) (2020)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Social Influence and the Collective Dynamics of Opinion Formation](https://mlsanity.com/paper/1311.3475/) (2013)
- [StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics](https://mlsanity.com/paper/2005.01575/) (2020)
- [Easy Hyperparameter Search Using Optunity](https://mlsanity.com/paper/1412.1114/) (2014)
- [VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization](https://mlsanity.com/paper/2012.01205/) (2020)
- [A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions](https://mlsanity.com/paper/2002.10998/) (2020)
- [Progressive Data Science: Potential and Challenges](https://mlsanity.com/paper/1812.08032/) (2018)
- [An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets](https://mlsanity.com/paper/2109.09160/) (2021)
- [Multivariate Data Explanation by Jumping Emerging Patterns Visualization](https://mlsanity.com/paper/2106.11112/) (2021)
- [An Interactive Machine Learning Framework](https://mlsanity.com/paper/1610.05463/) (2016)
- [ExplorerTree: a focus+context exploration approach for 2D embeddings](https://mlsanity.com/paper/2106.10592/) (2021)
- [RuleVis: Constructing Patterns and Rules for Rule-Based Models](https://mlsanity.com/paper/1911.04638/) (2019)

## Built on by (most cited first, 8 of 8)

- [TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization](https://mlsanity.com/paper/2209.09227/) (2022)
- [HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques](https://mlsanity.com/paper/2203.15753/) (2022)
- [Visual Analytics for Explainable and Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2507.10240/) (2025)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)
- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)
- [RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers](https://mlsanity.com/paper/2409.03164/) (2024)
- [Cluster-Based Random Forest Visualization and Interpretation](https://mlsanity.com/paper/2507.22665/) (2025)
- [Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics](https://mlsanity.com/paper/2607.00969/) (2026)

## Abstract (arXiv)

Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms, such as random forest and adaptive boosting, reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.