Optimal decision trees for categorical data via integer programming

• Published in: Journal of global optimization Vol. 81; no. 1; pp. 233 - 260
• Main Authors: Günlük, Oktay, Kalagnanam, Jayant, Li, Minhan, Menickelly, Matt, Scheinberg, Katya
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer; Springer Nature B.V
• Subjects: binary classification; Combinatorial analysis; Computer Science; Decision trees; Integer programming; machine learning; Mathematics; MATHEMATICS AND COMPUTING; Mathematics and Statistics; Mixed integer; Operations Research/Decision Theory; Optimization; Prediction models; Real Functions; Robustness (mathematics); S.i.: Gerad-40; Integer programming; Decision trees; Binary classification; Machine learning

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this pap...