vip: Variable Importance Plots

A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Version: 0.2.2
Imports: ggplot2 (≥ 0.9.0), gridExtra, magrittr, plyr, stats, tibble, utils
Suggests: DT, C50, caret, Ckmeans.1d.dp, covr, Cubist, doParallel, dplyr, earth, fastshap, gbm, glmnet, h2o, htmlwidgets, keras, knitr, lattice, mlbench, mlr, mlr3, neuralnet, NeuralNetTools, nnet, parsnip, party, partykit, pdp, pls, randomForest, ranger, rmarkdown, rpart, RSNNS, sparkline, sparklyr (≥ 0.8.0), tinytest, varImp, xgboost
Published: 2020-04-06
Author: Brandon Greenwell ORCID iD [aut, cre], Brad Boehmke ORCID iD [aut], Bernie Gray ORCID iD [aut]
Maintainer: Brandon Greenwell <greenwell.brandon at gmail.com>
BugReports: https://github.com/koalaverse/vip/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/koalaverse/vip/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: vip results

Downloads:

Reference manual: vip.pdf
Package source: vip_0.2.2.tar.gz
Windows binaries: r-devel: vip_0.2.2.zip, r-release: vip_0.2.2.zip, r-oldrel: vip_0.2.2.zip
macOS binaries: r-release: vip_0.2.2.tgz, r-oldrel: vip_0.2.2.tgz
Old sources: vip archive

Reverse dependencies:

Reverse imports: moreparty
Reverse suggests: pdp

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