Nathan Justin – Optimization Online

Learning Optimal Classification Trees Robust to Distribution Shifts

Published: 2023/10/26

(Mixed) Integer Linear Programming, Robust Optimization decision trees, distribution shift, mixed-integer programming, robust machine learning, robust optimization

We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high stakes settings such as public health and social work where data is often collected using self-reported surveys which are highly sensitive to e.g., the framing of the questions, the time … Read more

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

Published: 2023/07/31

(Mixed) Integer Linear Programming, Optimization Software and Modeling Systems classification trees, distribution shifts, fair classification trees, mixed integer optimization, open source software, prescriptive trees, robust classification trees

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, … Read more