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]

MulvariateRandomForestVarImp_0.0.6.tar.gz
MulvariateRandomForestVarImp_0.0.6.zip(r-4.7-any)MulvariateRandomForestVarImp_0.0.6.zip(r-4.6-any)MulvariateRandomForestVarImp_0.0.6.zip(r-4.5-any)
MulvariateRandomForestVarImp_0.0.6.tgz(r-4.6-any)MulvariateRandomForestVarImp_0.0.6.tgz(r-4.5-any)
MulvariateRandomForestVarImp_0.0.6.tar.gz(r-4.7-any)MulvariateRandomForestVarImp_0.0.6.tar.gz(r-4.6-any)
MulvariateRandomForestVarImp_0.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MulvariateRandomForestVarImp/json (API)

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

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

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

On CRAN:

Conda:

3.18 score 3 scripts 229 downloads 2 exports 4 dependencies

Last updated from:327ce45e23. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK112
source / vignettesOK141
linux-releaseOK101
macos-releaseOK206
macos-oldrelOK202
windows-develOK68
windows-releaseOK66
windows-oldrelOK70
wasm-releaseOK80

Exports:MeanOutcomeDifferenceMeanSplitImprovement

Dependencies:bootstrapMASSMultivariateRandomForestRcpp