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

• Main Authors: Vossler, Patrick, Aghaei, Sina, Justin, Nathan, Jo, Nathanael, Gómez, Andrés, Vayanos, Phebe
• Format: Journal Article
• Language: English
• Published: 28-07-2023
• Subjects: Computer Science - Learning; Mathematics - Optimization and Control; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2307.15691

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 mixed-integer optimization (MIO) framework proposed in Aghaei et al.
(2019) and several of its extensions. The current version of the package
provides implementations for learning optimal classification trees, optimal
fair classification trees, optimal classification trees robust to distribution
shifts, and optimal prescriptive trees from observational data. 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 and an extensive user guide can be found at
https://d3m-research-group.github.io/odtlearn/. Additionally, users can view
the package source code and submit feature requests and bug reports by visiting
https://github.com/D3M-Research-Group/odtlearn.