AFIT-R/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).

Getting started

Package details

Maintainer
LicenseGPL (>= 2)
Version0.4.1
URL https://github.com/koalaverse/vip/ https://koalaverse.github.io/vip/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("AFIT-R/vip")
AFIT-R/vip documentation built on Aug. 22, 2023, 8:59 a.m.