发表机构
Thomas Jefferson High School for Science and Technology; Lynbrook High School; Stanford University（托马斯·杰斐逊科技高中; 林布鲁克高中; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种学习型前瞻分裂规则用于CART，通过评估候选分裂的后续误差减少并利用节点特征学习下游分裂，提升层次或交互驱动场景下的分裂选择，同时保持可解释性。
AI 中文摘要
分类与回归树通常采用贪心分裂规则构建，该规则在每个节点最大化预测误差的即时减少。尽管此策略计算高效，但它可能错过那些短期收益较小但通过进一步划分能带来显著下游改进的分裂。我们提出一种前瞻树构建方法，通过在该分裂下方生长一个常规CART子树后所实现的预测误差减少来评估每个候选分裂。由于完整的前瞻过程可能计算代价高昂，我们还描述了一种智能前瞻算法，该算法利用节点级特征学习下游分裂值。所提出的框架保留了递归划分的可解释性，同时改进了层次或交互驱动设置中的分裂选择。我们进行了一项模拟研究，在多种设置下比较了常规、完整前瞻和智能前瞻方法，并将所提出的方法应用于分析两个真实数据示例，证明了新方法的优点。
英文摘要
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.
