Dataset Information

A Random Forest Approach for Bounded Outcome Variables.

ABSTRACT: Random forests have become an established tool for classification and regression, 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 suffer 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 compared to random forest approaches based on mean squared error loss and parametric regression models.

SUBMITTER: Weinhold L

PROVIDER: S-EPMC8193767 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Random Forest Approach for Bounded Outcome Variables.

Weinhold Leonie L Schmid Matthias M Mitchell Richard R Maloney Kelly O KO Wright Marvin N MN Berger Moritz M

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20200101 3

Random forests have become an established tool for classification and regression, 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 suffer 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 ...[more]

PMID: 34121830

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Dataset Information

A Random Forest Approach for Bounded Outcome Variables.

Publications

A Random Forest Approach for Bounded Outcome Variables.

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