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vip: Variable Importance Plots

Overview

vip is an R package for constructing variable importance plots (VIPs). VIPs are part of a larger framework referred to as interpretable machine learning (IML), which includes (but not limited to): partial dependence plots (PDPs) and individual conditional expectation (ICE) curves. While PDPs and ICE curves (available in the R package pdp) help visualize feature effects, VIPs help visualize feature impact (either locally or globally). An in-progress, but comprehensive, overview of IML can be found here: https://github.com/christophM/interpretable-ml-book.

Installation

# The easiest way to get vip is to install it from CRAN:
install.packages("vip")

# Alternatively, you can install the development version from GitHub:
if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_github("koalaverse/vip")

For details and example usage, visit the vip package website.

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Version

Install

install.packages('vip')

Monthly Downloads

11,042

Version

0.1.2

License

GPL (>= 2)

Issues

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Maintainer

Brandon Greenwell

Last Published

September 30th, 2018

Functions in vip (0.1.2)

vi_ice

ICE-Based Variable Importance
vint

Interaction Effects
get_feature_names

Extract Predictor Names
vip

Variable Importance Plots
grid.arrange

Arrange multiple grobs on a page
%T>%

Pipe operator
vi

Variable Importance
vi_pdp

PDP-Based Variable Importance
vi_permute

Permutation-Based Variable Importance
vi_model

Model-Based Variable Importance