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 
[aut, cre],
Brad Boehmke
[aut],
Bernie Gray [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 |