# TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

arXiv: https://arxiv.org/abs/2209.09227 · HTML: https://mlsanity.com/paper/2209.09227/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Optimal Sparse Decision Trees](https://mlsanity.com/paper/1904.12847/) (2019)
- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [How Interpretable and Trustworthy are GAMs?](https://mlsanity.com/paper/2006.06466/) (2020)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values](https://mlsanity.com/paper/2206.15465/) (2022)
- [Accuracy, Interpretability, and Differential Privacy via Explainable Boosting](https://mlsanity.com/paper/2106.09680/) (2021)
- [Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles](https://mlsanity.com/paper/2005.04289/) (2020)
- [GAM Changer: Editing Generalized Additive Models with Interactive Visualization](https://mlsanity.com/paper/2112.03245/) (2021)
- [StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics](https://mlsanity.com/paper/2005.01575/) (2020)
- [Forward Stability and Model Path Selection](https://mlsanity.com/paper/2103.03462/) (2021)
- [VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees](https://mlsanity.com/paper/2112.00334/) (2021)
- [Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures](https://mlsanity.com/paper/1908.00387/) (2019)
- [A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions](https://mlsanity.com/paper/2002.10998/) (2020)
- [StickyLand: Breaking the Linear Presentation of Computational Notebooks](https://mlsanity.com/paper/2202.11086/) (2022)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)

## Built on by (most cited first, 13 of 13)

- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [A Path to Simpler Models Starts With Noise](https://mlsanity.com/paper/2310.19726/) (2023)
- [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://mlsanity.com/paper/2302.14165/) (2023)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers](https://mlsanity.com/paper/2409.03164/) (2024)
- [Composable Building Blocks for Controllable and Transparent Interactive AI Systems](https://mlsanity.com/paper/2506.02262/) (2025)
- [Cluster-Based Random Forest Visualization and Interpretation](https://mlsanity.com/paper/2507.22665/) (2025)
- [Why are there many equally good models? An Anatomy of the Rashomon Effect](https://mlsanity.com/paper/2601.06730/) (2026)
- [Improving Decision Sparsity](https://mlsanity.com/paper/2410.20483/) (2024)
- [High-Dimensional Data Classification in Concentric Coordinates](https://mlsanity.com/paper/2507.18450/) (2025)
- [VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance](https://mlsanity.com/paper/2507.22556/) (2025)
- [MATCH: Engineering Transparent and Controllable Conversational XAI Systems through Composable Building Blocks](https://mlsanity.com/paper/2511.22420/) (2025)

## Abstract (arXiv)

Given thousands of equally accurate machine learning (ML) models, how can users choose among them? A recent ML technique enables domain experts and data scientists to generate a complete Rashomon set for sparse decision trees--a huge set of almost-optimal interpretable ML models. To help ML practitioners identify models with desirable properties from this Rashomon set, we develop TimberTrek, the first interactive visualization system that summarizes thousands of sparse decision trees at scale. Two usage scenarios highlight how TimberTrek can empower users to easily explore, compare, and curate models that align with their domain knowledge and values. Our open-source tool runs directly in users' computational notebooks and web browsers, lowering the barrier to creating more responsible ML models. TimberTrek is available at the following public demo link: https://poloclub.github.io/timbertrek.