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] (<>), Brad Boehmke [aut] (<>), Bernie Gray [aut] (<>)
MaintainerBrandon Greenwell <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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vip documentation built on Dec. 17, 2020, 5:08 p.m.