Package: MulvariateRandomForestVarImp 0.0.6

Dogonadze Nika

MulvariateRandomForestVarImp: Variable Importance Measures for Multivariate Random Forests

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

Authors:Nika Dogonadze [cre], Giles Hooker [aut], Vrinda Kadiyali [ctb], Sharmistha Sikdar [aut]

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MulvariateRandomForestVarImp.pdf |MulvariateRandomForestVarImp.html
MulvariateRandomForestVarImp/json (API)

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

Peer review:

Bug tracker:https://github.com/megatvini/vim/issues

Datasets:
  • EbirdData - Training and Test sets for Multispecies Ebird data

On CRAN:

3.65 score 4 scripts 183 downloads 2 exports 4 dependencies

Last updated 3 months agofrom:327ce45e23. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:MeanOutcomeDifferenceMeanSplitImprovement

Dependencies:bootstrapMASSMultivariateRandomForestRcpp