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A random forest approach for bounded outcome variables

Journal of Computational and Graphical Statistics
By: , , , , , and 
https://doi.org/10.1080/10618600.2019.1705310

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Abstract

Random forests have become an established tool for classication and regres- sion, in particular in high-dimensional settings and in the presence of non-additive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta dis- tributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits dur- ing the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates de- rived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when com- pared to random forest approaches based on mean squared error loss and parametric regression models.
Publication type Article
Publication Subtype Journal Article
Title A random forest approach for bounded outcome variables
Series title Journal of Computational and Graphical Statistics
DOI 10.1080/10618600.2019.1705310
Volume 29
Issue 3
Year Published 2020
Language English
Publisher Taylor and Francis
Contributing office(s) Leetown Science Center
Description 20 p.
First page 639
Last page 658
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