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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 inter-pretable ML models. To help ML practitioners identify models with desirable properties from this Rashomon set, we develop Tim-bertrek, 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&#x0027; 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