Near-optimal Nonlinear Regression Trees

被引：10

作者：

Bertsimas, Dimitris ^{[1
]}

Dunn, Jack ^{[2
]}

Wang, Yuchen ^{[2
]}

机构：

[1] MIT, Sloan Sch Management, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[2] MIT, Ctr Operat Res, Cambridge, MA 02139 USA

来源：

OPERATIONS RESEARCH LETTERS | 2021年 / 49卷 / 02期

关键词：

Decision trees; Regression; Nonlinear optimization;

D O I：

10.1016/j.orl.2021.01.002

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We propose Near-optimal Nonlinear Regression Trees with hyperplane splits (NNRTs) that use a polynomial prediction function in the leaf nodes, which we solve by stochastic gradient methods. On synthetic data, we show experimentally that the algorithm converges to the global optimal. We compare NNRTs, ORT-LH, Multivariate Adaptive Regression Splines (MARS), Random Forests (RF) and XGBoost on 40 real-world datasets and show that overall NNRTs have a performance edge over all other methods. (C) 2021 Published by Elsevier B.V.

引用

页码：201 / 206

页数：6

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