vip (0.1.3)

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Variable Importance Plots.

https://koalaverse.github.io/vip/index.html
https://github.com/koalaverse/vip/
http://cran.r-project.org/web/packages/vip

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 an efficient permutation-based variable importance measure as well as novel approaches based on partial dependence plots (PDPs) and individual conditional expectation (ICE) curves which are described in Greenwell et al. (2018) . An experimental method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Maintainer: Brandon M. Greenwell
Author(s): Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Bernie Gray [aut] (<https://orcid.org/0000-0001-9190-6032>)

License: GPL (>= 2)

Uses: ggplot2, gridExtra, magrittr, ModelMetrics, pdp, plyr, tibble, caret, earth, gbm, lattice, mlbench, party, randomForest, rpart, glmnet, neuralnet, nnet, testthat, Ckmeans.1d.dp, RSNNS, Cubist, partykit, doParallel, knitr, C50, dplyr, h2o, xgboost, rmarkdown, htmlwidgets, NeuralNetTools, covr, DT, ranger, sparklyr, sparkline, keras, varImp
Reverse suggests: pdp