mlsanityData to 7 Oct 2026

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

arXiv 2209.09227 ↗First posted 19 Sep 2022cs.HC cs.AI cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 21 of its 54 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

21 of 54 references.

References · 21

  1. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  2. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  3. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  4. 2019Optimal Sparse Decision Treescited by 104
  5. 2020Generalized and Scalable Optimal Sparse Decision Treescited by 98
  6. 2022Exploring the Whole Rashomon Set of Sparse Decision Treescited by 80
  7. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
  8. 2016Learning Optimized Risk Scorescited by 47
  9. 2020How Interpretable and Trustworthy are GAMs?cited by 44
  10. 2020In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictioncited by 42
  11. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  12. 2021Accuracy, Interpretability, and Differential Privacy via Explainable Boostingcited by 24
9 more
  1. 2020Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensemblescited by 23
  2. 2021GAM Changer: Editing Generalized Additive Models with Interactive Visualizationcited by 18
  3. 2020StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metricscited by 13
  4. 2021Forward Stability and Model Path Selectioncited by 12
  5. 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescited by 8
  6. 2019Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architecturescited by 7
  7. 2020A Visual Analytics System for Multi-model Comparison on Clinical Data Predictionscited by 7
  8. 2022StickyLand: Breaking the Linear Presentation of Computational Notebookscited by 6
  9. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 5; 2026: 1.

13 papers.

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  1. 2024Amazing Things Come From Having Many Good Modelscited by 52
  2. 2023A Path to Simpler Models Starts With Noisecited by 33
  3. 2023GAM Coach: Towards Interactive and User-centered Algorithmic Recoursecited by 9
  4. 2023SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebookscited by 3
  5. 2023Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modelingcited by 1
  6. 2024RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifierscited by 1
  7. 2025Composable Building Blocks for Controllable and Transparent Interactive AI Systemscited by 1
  8. 2025Cluster-Based Random Forest Visualization and Interpretationcited by 1
  9. 2026Why are there many equally good models? An Anatomy of the Rashomon Effectcited by 1
  10. 2024Improving Decision Sparsitycited by 0
  11. 2025High-Dimensional Data Classification in Concentric Coordinatescited by 0
  12. 2025VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performancecited by 0
1 more
  1. 2025MATCH: Engineering Transparent and Controllable Conversational XAI Systems through Composable Building Blockscited by 0