vip: Variable Importance Plots

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 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Package details

AuthorBrandon 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>)
MaintainerBrandon Greenwell <greenwell.brandon@gmail.com>
LicenseGPL (>= 2)
Version0.3.2
URL https://github.com/koalaverse/vip/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("vip")

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vip documentation built on Dec. 17, 2020, 5:08 p.m.