Bias corrections for Random Forest in regression using residual rotation

Jongwoo Song

Research output: Contribution to journalArticlepeer-review

26 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 languageEnglish
Pages (from-to)321-326
Number of pages6
JournalJournal of the Korean Statistical Society
Volume44
Issue number2
DOIs
StatePublished - Jun 2015

Keywords

  • Bias correction
  • Random Forest
  • Regression

Fingerprint

Dive into the research topics of 'Bias corrections for Random Forest in regression using residual rotation'. Together they form a unique fingerprint.

Cite this