In the era of Explainable Artificial Intelligence, there is a renewed focus on single trees for their ease of interpretation. This paper introduces Simultaneous Latent Budget Trees, a probabilistic machine learning framework for classification trees in the presence of a stratification factor such as a temporal, spatial, or demographic variable, acting as a control variable or potential confounder.
The paper introduces a look‑ahead splitting rule for Classification and Regression Trees (CART) that evaluates candidate splits by the error reduction achieved after growing a conventional CART subtree beneath each split. To keep the method computationally feasible, a smart look‑ahead algorithm is proposed that learns downstream split values from node‑level features. Experiments on simulated data and two real datasets show that both full and smart look‑ahead methods outperform the standard greedy splitting strategy, especially in hierarchical or interaction‑driven scenarios.
By Andrew Gao, Tianlin Liu, Ruichen Han, Lu Tian
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: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
arXiv:2608.24195v1 Announce Type: cross
Abstract: Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through a...
By Soham Chatterjee, Rwitobroto Dey, Smarajit Bose
The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.
By Nicolas Alexander Ihlo, Merle Behr
arXiv:2609.24528v1 Announce Type: cross
Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs...
By Lea L. Mairh\"ofer, Silke Szymczak, Bj\"orn-Hergen Laabs, Tuwe L\"ofstr\"om-Cavallin
arXiv:2609.36532v1 Announce Type: cross
Abstract: In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a...
By Futoshi Futami, Jerry Huang, Ichiro Takeuchi
Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models...
arXiv:2412. 16209v5 Announce Type: replace Abstract: When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset.
By Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
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
arXiv:2608. 06195v1 Announce Type: cross Abstract: Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments.
By Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi