Learned Look-Ahead Splitting Rule for CART

2026-09-14

AI Summary

提出了一种前瞻性的CART树构建方法，通过预测误差减少评估每个候选分裂点，并使用节点特征学习下游分裂值以提高计算效率。

Abstract

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 computationally efficient, it can miss splits that yield small short-term gains but create substantial downstream improvements after further partitioning. We propose a look-ahead tree-building method that evaluates each candidate split by the prediction error reduction achieved after growing a conventional CART subtree below that split. Because the full look-ahead procedure can be computationally expensive, we also describe a smart look-ahead algorithm that learns downstream split values using node-level features. The proposed framework preserves the interpretability of recursive partitioning while improving split selection in hierarchical or interaction-driven settings. We conduct a simulation study comparing conventional, full look-ahead, and smart look-ahead methods under several settings and apply the proposed methods to analyze two real data examples demonstrating the merit of the new methods.

Problem

• CART
• splitting rule
• prediction error
• greedy algorithm
• look-ahead

Innovation

• look-ahead splitting rule
• prediction error reduction
• smart look-ahead algorithm
• node-level features