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: |
0.1.2 |
| Imports: |
ggplot2 (≥ 0.9.0), gridExtra, magrittr, ModelMetrics, pdp, plyr, stats, tibble, utils |
| Suggests: |
C50, caret, Ckmeans.1d.dp, covr, doParallel, dplyr, earth, gbm, glmnet, h2o, keras, knitr, lattice, mlbench, NeuralNetTools, nnet, party, partykit, randomForest, ranger, rmarkdown, rpart, sparklyr, testthat, xgboost |
| Published: |
2018-09-30 |
| 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://koalaverse.github.io/vip/index.html,
https://github.com/koalaverse/vip/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
vip results |