arXiv Machine Learning By Patrick Vossler, Nathan Justin, Sina Aghaei, Nathanael Jo, Andr\'es G\'omez, Phebe Vayanos

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

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arXiv:2307. 15691v4 Announce Type: replace-cross 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 state-of-the-art mixed-integer optimization (MIO) framework proposed in Aghaei et al.

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arXiv Machine Learning
Aug 7

Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance

arXiv:2409. 12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric.

By Jacobus G. M. van der Linden, Dani\"el Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirovi\'c
arXiv AI
Jul 22

MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers

arXiv:2607. 18252v1 Announce Type: new Abstract: Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model.

By Jinbiao Nie, Kewei Feng, Xiaoyuan Zhang, Shan Yin, Zizhuo Wang, Bin Dong
arXiv AI
3d ago

A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees

The paper introduces a moving-horizon approximate branch‑and‑reduce method for training deep classification trees on large datasets with continuous features. It combines a hierarchical root‑subtree optimization framework, branch‑and‑reduce at the root, greedy heuristics for subtrees, and a low‑cost moving‑horizon refinement to improve accuracy. Experiments show the approach surpasses heuristic baselines in test accuracy while scaling better in dataset size and tree depth than existing global optimal solvers.

By Chenxuanyin Zou, Jiayang Ren, Qiangqiang Mao, Jing Liu, Marcus Lai, Yankai Cao