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

Authors

334

  1. CS
    Carolin Strobl

    University of Zurich, Switzerland

    4 publications · 7.2K citations

    4

    h-index

  2. XS
    Xiaogang Su

    The University of Texas at El Paso, United States

    4 publications · 562 citations

    4

    h-index

  3. BM
    Bertrand Michel

    Laboratoire de Statistique Théorique et Appliquée, France

    3 publications · 1.2K citations

    3

    h-index

  4. PG
    Pierre Geurts

    University of Liège, Belgium

    3 publications · 906 citations

    3

    h-index

  5. AZ
    Achim Zeileis

    Universität Innsbruck, Austria

    3 publications · 7K citations

    3

    h-index

1–5 of 334

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 129 citations

    4

    h-index

  2. BMC Bioinformatics

    4 publications · 7K citations

    4

    h-index

  3. Applied Sciences

    3 publications · 77 citations

    3

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 3 publications · 1.3K citations

    3

    h-index

  5. Electronic Journal of Statistics

    2 publications · 379 citations

    2

    h-index

1–5