Linear Aggregation in Tree-Based Estimators
研究了回归树和随机森林中使用线性聚合函数的方法，提出新算法以拟合节点上的线性聚合，提升对局部平滑响应面的预测性能和可解释性，并通过模拟和实验验证效果。
Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve their utility for locally smooth response surfaces, we study regression trees and random forests with linear aggregation functions. We introduce a new algorithm that finds the best axis-aligned split to fit linear aggregation functions on the corresponding nodes, and we offer a quasilinear time implementation. We demonstrate the algorithm's favorable performance on real-world benchmarks and in an extensive simulation study, and we demonstrate its improved interpretability using a large get-out-the-vote experiment. We provide an open-source software package that implements several tree-based estimators with linear aggregation functions. Supplementary materials for this article are available online.