Bias corrections for Random Forest in regression using residual rotation

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. (C) 2015 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.