Linear Aggregation in Tree-based Estimators

Sören R. Künzel; Theo F. Saarinen; Edward W. Liu; Jasjeet S. Sekhon

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arXiv · 1906.06463

Linear Aggregation in Tree-based Estimators

Sören R. Künzel·Theo F. Saarinen·Edward W. Liu·Jasjeet S. Sekhon

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Abstract

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.

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BibTeX RIS

Sören R. Künzel, Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon. 2019-06-15. Linear Aggregation in Tree-based Estimators. https://arxiv.org/abs/1906.06463

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