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

TL;DR

ODTlearn is an open-source Python package that enables learning optimal decision trees for various high-stakes predictive and prescriptive tasks using mixed-integer optimization, supporting multiple problem types and solvers.

Contribution

It introduces a flexible, extendable software package for optimal decision trees based on MIO, accommodating new problem classes and solution methods.

Findings

Supports learning optimal classification and prescriptive trees

Includes extensions for fair and robust decision trees

Compatible with Gurobi and COIN-OR solvers

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 mixed-integer optimization (MIO) framework proposed in (Aghaei et al., 2021) 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.