Bias corrections for Random Forest in regression using residual rotation

Jongwoo Song

doi:10.1016/j.jkss.2015.01.003

Bias corrections for Random Forest in regression using residual rotation

Jongwoo Song

Department of Statistics

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly.

Original language	English
Pages (from-to)	321-326
Number of pages	6
Journal	Journal of the Korean Statistical Society
Volume	44
Issue number	2
DOIs	https://doi.org/10.1016/j.jkss.2015.01.003
State	Published - Jun 2015

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2013R1A1A2012817 ).

Publisher Copyright:
© 2015 The Korean Statistical Society.

Keywords

Bias correction
Random Forest
Regression

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10.1016/j.jkss.2015.01.003

Cite this

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title = "Bias corrections for Random Forest in regression using residual rotation",

abstract = "This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly.",

keywords = "Bias correction, Random Forest, Regression",

author = "Jongwoo Song",

note = "Funding Information: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2013R1A1A2012817 ). Publisher Copyright: {\textcopyright} 2015 The Korean Statistical Society.",

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PY - 2015/6

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N2 - This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly.

AB - This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest significantly.

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KW - Regression

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JF - Journal of the Korean Statistical Society

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ER -

Bias corrections for Random Forest in regression using residual rotation

Abstract

Bibliographical note

Keywords

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