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arXiv · 2307.15691

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

Patrick Vossler·Nathan Justin·Sina Aghaei·Nathanael Jo·Andrés Gómez·Phebe Vayanos
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Abstract

ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2025). The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, optimal prescriptive trees from observational data, and optimal classification trees robust to distribution shifts. We have designed the package to be easy to maintain and extend as new optimal decision tree problem classes, reformulation strategies, and solution algorithms are introduced. To this end, the package follows object-oriented design principles and supports both commercial (Gurobi) and open source (COIN-OR branch and cut) solvers. The package documentation, user guide, installation instructions, link to source code, and instructions for submitting bug reports and feature requests can all be found at https://d3m-research-group.github.io/odtlearn/.

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Patrick Vossler, Nathan Justin, Sina Aghaei, Nathanael Jo, Andrés Gómez, Phebe Vayanos. 2023-07-28. ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription. https://arxiv.org/abs/2307.15691

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