arXiv:2608. 20258v1 Announce Type: new Abstract: Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance.
By MD Saifur Rahman Mazumder, Feng Yu
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
By Sabino Francesco Roselli, Eibe Frank
The paper introduces ICOMT, a framework for interpretable clustering using optimal multi-way decision trees. It proposes a new discretization technique based on one-dimensional K‑means, formulates a binary linear optimization problem to ensure tree optimality, and demonstrates superior clustering accuracy and shallow tree structures on four public datasets.
By Hayato Suzuki, Shunnosuke Ikeda, Naoki Nishimura, Yuichi Takano
Classification and regression trees are typically constructed using a greedy splitting rule that maximizes the immediate reduction in prediction error at each node. Although this strategy is computati...
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
By Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirovi\'c
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin