Decision trees are powerful supervised machine learning tools for classification and regression that attract many academic researchers and industry professionals. In particular, decision trees provide interpretability, which is often preferred over other higher-accuracy methods that are relatively uninterpretable. In this talk, we explore discrete optimization techniques to derive classification trees. In particular, we consider binary classification trees where we introduce mixed-integer linear optimization (MILO) formulations that offer theoretical improvements on the strongest flow-based MILO formulation currently in the literature and conduct experiments on publicly available data sets to demonstrate our models’ ability to scale, strength against traditional branch-and-bound approaches, and robustness in out-of-sample test performance.