A general framework for constructing variable importance plots from
various types machine learning models in R. Aside from some standard model-
based variable importance measures, this package also provides a novel
approach based on partial dependence plots (PDPs) and individual conditional
expectation (ICE) curves as described in Greenwell et al. (2018)
<arXiv:1805.04755>.
| Version: |
0.1.0 |
| Imports: |
dplyr, ggplot2 (≥ 0.9.0), gridExtra, magrittr, pdp, plyr, stats, tibble, tidyr, utils |
| Suggests: |
C50, caret, earth, gbm, h2o, knitr, party, partykit, ranger, rpart, randomForest, rmarkdown, xgboost, glmnet, testthat |
| Published: |
2018-06-15 |
| Author: |
Brandon Greenwell  [aut, cre],
Brad Boehmke [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 |