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Haozhe Zhang

Iowa State University



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Dan Nettleton

Iowa State University, Ames, Iowa



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Zhengyuan Zhu

Iowa State University



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461 – SPEED: Machine Learning

Regression-Enhanced Random Forests

Sponsor: Section on Statistical Learning and Data Science
Keywords: machine learning, prediction, Lasso, random forests, extrapolation

Haozhe Zhang

Iowa State University

Dan Nettleton

Iowa State University, Ames, Iowa

Zhengyuan Zhu

Iowa State University

Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely regression-enhanced random forests (RERFs), that can improve on RFs by borrowing the strength of penalized parametric regression. The algorithm for constructing RERFs and selecting its tuning parameters is described. Both simulation study and real data examples show that RERFs have better predictive performance than RFs in important situations often encountered in practice. Moreover, RERFs may incorporate known relationships between the response and the predictors, and may give reliable predictions in extrapolation problems where predictions are required at points out of the domain of the training dataset. Strategies analogous to those described here can be used to improve other machine learning methods via combination with penalized parametric regression techniques.

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