arXiv Machine Learning September 16, 2026 By Andrew Gao, Tianlin Liu, Ruichen Han, Lu Tian

Learned Look-Ahead Splitting Rule for CART

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

safety

Hugging Face Trending Papers

Sep 14

Learned Look-Ahead Splitting Rule for CART

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...

safety

arXiv Machine Learning

Jun 24

Experiments with Optimal Model Trees

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

benchmarks safety

arXiv AI

Sep 1

Adaptive Multi-Branching for Shallow Decision Tree Induction

arXiv:2608.29262v1 Announce Type: cross Abstract: Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Unde...

By Hanul Park, Jeonghoon Choi, Juseong Kim, Sanghun Sel, Giltae Song

efficiency benchmarks

Hugging Face Trending Papers

Aug 20

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

The paper introduces DICS, a clustering-based framework that uses data-informed priors to construct a compact set of candidate splits for decision tree classifiers. By incorporating class-aware structure, DICS reduces the split search space, preserving predictive performance while cutting training time. The authors provide theoretical analysis and experimental results showing comparable accuracy to exhaustive search across synthetic and benchmark datasets.

benchmarks safety

arXiv Machine Learning

Aug 21

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

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

benchmarks safety

Hugging Face Trending Papers

Jun 11

CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees

Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing well-performing regression trees.

efficiency

One email a morning, machine-written

Related stories

Learned Look-Ahead Splitting Rule for CART

Experiments with Optimal Model Trees

Adaptive Multi-Branching for Shallow Decision Tree Induction

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees