TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

TL;DR

TimberTrek is an interactive visualization tool that helps users explore, compare, and select from large sets of sparse decision trees generated by recent ML techniques, facilitating more responsible and interpretable model choices.

Contribution

This paper introduces TimberTrek, the first scalable interactive visualization system for exploring and curating large sets of sparse decision trees from the Rashomon set.

Findings

Enables users to identify models with desirable properties easily.

Supports exploration and comparison of thousands of models.

Runs seamlessly in notebooks and web browsers.

Abstract

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.