Tree-based Variable Importance
Tree-Based Variable Importance is a systematic measure that quantifies the relative contribution of individual predictor variables to the predictive performance of a decision tree model based on the frequency and impact of their splits.
105
Publications
21.9K
Citations
334
Authors
180
Institutions
Publications per year
2017–2026
47
Variants
Tree-based Feature Importance
Parents
105
Gérard Biau, Erwan Scornet · Test · 2016 · 3.9K citations · Full text
Conditional variable importance for random forests
Carolin Strobl, Anne‐Laure Boulesteix, Thomas Kneib et al. · BMC Bioinformatics · 2008 · 3.2K citations · Full text
Correlation and variable importance in random forests
Baptiste Gregorutti, Bertrand Michel, Philippe Pierre · Statistics and Computing · 2016 · 1.1K citations · Full text
Computing, Ecology, Data Mining +9
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334
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Laboratoire de Statistique Théorique et Appliquée, France
3 publications · 1.2K citations
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3
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3
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1–5 of 334
180
Ludwig-Maximilians-Universität München
Munich, Germany
6 h-index
12 publications
14.2K citations

University of Central Florida
Orlando, United States
5 h-index
8 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
8 publications
9.7K citations
Laboratoire de Statistique Théorique et Appliquée
Paris, France
4 h-index
11 publications
11.3K citations

Stellenbosch University
Stellenbosch, South Africa
3 h-index
6 publications
380 citations
University of Liège
Liège, Belgium
3 h-index
11 publications
3.6K citations
Cleveland Clinic
Cleveland, United States
3 h-index
4 publications
780 citations

Cornell University
Ithaca, United States
3 h-index
5 publications
155 citations
1–8 of 180
Repository · 4 publications · 129 citations
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4 publications · 7K citations
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3 publications · 77 citations
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CSDA · 3 publications · 1.3K citations
3
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2 publications · 379 citations
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