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) <doi:10.48550/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: |
0.4.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
foreach, ggplot2 (≥ 0.9.0), stats, tibble, utils, yardstick |
| Suggests: |
bookdown, DT, covr, doParallel, dplyr, fastshap (≥ 0.1.0), knitr, lattice, mlbench, modeldata, NeuralNetTools, pdp, rmarkdown, tinytest (≥ 1.4.1), varImp |
| Enhances: |
C50, caret, Cubist, earth, gbm, glmnet, h2o, lightgbm, mixOmics, mlr, mlr3, neuralnet, nnet, parsnip (≥ 0.1.7), party, partykit, pls, randomForest, ranger, rpart, RSNNS, sparklyr (≥ 0.8.0), tidymodels, workflows (≥ 0.2.3), xgboost |
| Published: |
2023-08-21 |
| Author: |
Brandon M. Greenwell
 [aut, cre],
Brad Boehmke
[aut] |
| Maintainer: |
Brandon M. 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/,
https://koalaverse.github.io/vip/ |
| NeedsCompilation: |
no |
| Citation: |
vip citation info |
| Materials: |
README NEWS |
| CRAN checks: |
vip results |