vip (0.1.2)

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Variable Importance Plots.

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) .

Maintainer: Brandon M. Greenwell
Author(s): Brandon Greenwell [aut, cre] (<>), Brad Boehmke [aut] (<>), Bernie Gray [aut]

License: GPL (>= 2)

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

Released 8 months ago.

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