Package: rfVarImpOOB 1.0.3

rfVarImpOOB: Unbiased Variable Importance for Random Forests

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi:10.1080/03610926.2020.1764042>. The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees.

Authors:Markus Loecher <[email protected]>

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rfVarImpOOB/json (API)

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

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

13 exports 0.00 score 70 dependencies 5 scripts 184 downloads

Last updated 2 years agofrom:60b5b1366b. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-winNOTEAug 25 2024
R-4.5-linuxNOTEAug 25 2024
R-4.4-winNOTEAug 25 2024
R-4.4-macNOTEAug 25 2024
R-4.3-winNOTEAug 25 2024
R-4.3-macNOTEAug 25 2024

Exports:Accuracygini_indexgini_processGiniImportanceForestGiniImportanceTreeInOutBagslpnormmloglossModeplotVIplotVI2preorder2rfTitanic

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarvergenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6randomForestrangerRColorBrewerRcppRcppEigenrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselecttitanicutf8vctrsviridisLitewithr

Variable Importance based on reduction of Gini on OOB

Rendered fromrfVarImpOOB-vignette.Rmdusingknitr::rmarkdownon Aug 25 2024.

Last update: 2020-10-18
Started: 2019-04-05