Package: randomForestExplainer 0.11.0

Yue Jiang

randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance

A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

Authors:Aleksandra Paluszynska [aut], Przemyslaw Biecek [aut, ths], Michael Mayer [aut], Olivier Roy [aut], Yue Jiang [aut, cre]

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randomForestExplainer.pdf |randomForestExplainer.html
randomForestExplainer/json (API)
NEWS

# Install 'randomForestExplainer' in R:
install.packages('randomForestExplainer', repos = c('https://modeloriented.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/modeloriented/randomforestexplainer/issues

Pkgdown site:https://modeloriented.github.io

On CRAN:

Conda:

random-forest

9.59 score 233 stars 236 scripts 1.0k downloads 20 mentions 11 exports 79 dependencies

Last updated 1 years agofrom:c92335e726. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winOKMar 17 2025
R-4.5-macOKMar 17 2025
R-4.5-linuxOKMar 17 2025
R-4.4-winOKMar 17 2025
R-4.4-macOKMar 17 2025
R-4.4-linuxOKMar 17 2025
R-4.3-winOKMar 17 2025
R-4.3-macOKMar 17 2025

Exports:explain_forestimportant_variablesmeasure_importancemin_depth_distributionmin_depth_interactionsplot_importance_ggpairsplot_importance_rankingsplot_min_depth_distributionplot_min_depth_interactionsplot_multi_way_importanceplot_predict_interaction

Dependencies:base64encbslibcachemclicolorspacecpp11crayoncrosstalkdata.tabledigestdplyrDTevaluatefansifarverfastmapfontawesomeforcatsfsgenericsGGallyggplot2ggrepelggstatsgluegtablehighrhmshtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepatchworkpillarpkgconfigplyrprettyunitsprogresspromisespurrrR6randomForestrangerrappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Understanding random forests with randomForestExplainer

Rendered fromrandomForestExplainer.Rmdusingknitr::rmarkdownon Mar 17 2025.

Last update: 2024-03-22
Started: 2017-07-12