Introduction | Data and forest | Distribution of minimal depth | Various variable importance measures | Multi-way importance plot | Compare measures using ggpairs | Compare different rankings | Variable interactions | Conditional minimal depth | Prediction of the forest on a grid | Explain the forest
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:
randomForestExplainer_0.11.0.tar.gz
randomForestExplainer_0.11.0.zip(r-4.7)randomForestExplainer_0.11.0.zip(r-4.6)randomForestExplainer_0.11.0.zip(r-4.5)
randomForestExplainer_0.11.0.tgz(r-4.6-any)randomForestExplainer_0.11.0.tgz(r-4.5-any)
randomForestExplainer_0.11.0.tar.gz(r-4.7-any)randomForestExplainer_0.11.0.tar.gz(r-4.6-any)
randomForestExplainer_0.11.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
randomForestExplainer/json (API)
| # 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/docs site:https://modeloriented.github.io
Last updated from:c92335e726. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 210 | ||
| source / vignettes | OK | 212 | ||
| linux-release-x86_64 | OK | 207 | ||
| macos-release-arm64 | OK | 157 | ||
| macos-oldrel-arm64 | OK | 111 | ||
| windows-devel | OK | 177 | ||
| windows-release | OK | 155 | ||
| windows-oldrel | OK | 159 | ||
| wasm-release | OK | 173 |
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:base64encbslibcachemclicpp11crayoncrosstalkdata.tabledigestdplyrDTevaluatefarverfastmapfontawesomeforcatsfsgenericsGGallyggplot2ggrepelggstatsgluegtablehighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMatrixmemoisemimeotelpatchworkpillarpkgconfigprettyunitsprogresspromisespurrrR6randomForestrangerrappdirsRColorBrewerRcppRcppEigenrlangrmarkdownS7sassscalesstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Explain a random forest | explain_forest |
| Extract k most important variables in a random forest | important_variables |
| Importance of variables in a random forest | measure_importance |
| Calculate minimal depth distribution of a random forest | min_depth_distribution |
| Calculate mean conditional minimal depth | min_depth_interactions |
| Plot importance measures with ggpairs | plot_importance_ggpairs |
| Plot importance measures rankings with ggpairs | plot_importance_rankings |
| Plot the distribution of minimal depth in a random forest | plot_min_depth_distribution |
| Plot the top mean conditional minimal depth | plot_min_depth_interactions |
| Multi-way importance plot | plot_multi_way_importance |
| Plot the prediction of the forest for a grid of values of two numerical variables | plot_predict_interaction |
