TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

crown jewel figure
TimberTrek empowers domain experts and data scientists to easily explore thousands of well-performing decision trees so they can find and collect those trees that best reflect their knowledge and values. Consider the task of predicting whether a criminal is likely to commit a crime in the next two years. (A) The Rashomon Overview visually summarizes all well-performing decision trees by organizing them based on their decision paths, enabling users to seamlessly transition across different model subsets and explore trees with similar prediction patterns. (B) Clicking a tree opens a repositionable Tree Window showing details of a decision tree: multiple windows allow users to compare several model candidates’ prediction patterns. (C) The Search Panel provides filtering tools, enabling users to quickly identify decision trees with desired properties, such as accuracy, robustness, simplicity, and used features.
Demo Video
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 high- light 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.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
  title = {{{TimberTrek}}: {{Exploring}} and {{Curating Sparse Decision Trees}} with {{Interactive Visualization}}},
  booktitle = {2022 {{IEEE Visualization Conference}} ({{VIS}})},
  author = {Wang, Zijie J. and Zhong, Chudi and Xin, Rui and Takagi, Takuya and Chen, Zhi and Chau, Duen Horng and Rudin, Cynthia and Seltzer, Margo},
  year = {2022}