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Correlated variable importance for random forests
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랜덤포레스트를 위한 상관예측변수 중요도

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Type
Academic journal
Author
Seung Beom Shin (고려대학교) Hyung Jun Cho (고려대학교)
Journal
한국통계학회 응용통계연구 Vol.34 No.2 KCI Accredited Journals
Published
2021.4
Pages
177 - 190 (14page)

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Correlated variable importance for random forests
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Research history (2)

2021

Correlated variable importance for random forests
Seung Beom Shin ,  Hyung Jun Cho 응용통계연구 2021.04 Academic journal
랜덤포레스트를 위한 상관예측변수 중요도
신승범 통계학과 2021.01 Thesis
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Abstract· Keywords

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Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which variable plays a role more importantly in constructing the random forests. However, when a predictor is correlated with other predictors, the variable importance of the existing importance algorithm may be distorted. The downward bias of correlated predictors may reduce the importance of truly important predictors. We propose a new algorithm remedying the downward bias of correlated predictors. The performance of the proposed algorithm is demonstrated by the simulated data and illustrated by the real data.
#random forests #variable importance #correlation #랜덤포레스트 #변수중요도 #상관관계

Contents

Abstract
1. 서론
2. 분류 변수중요도
3. 모의실험
4. 사례분석
5. 결론
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