Variable Importance Plots—An Introduction to the vip Package

In the era of “big data”, it is becoming more of a challenge to not only build state-of-the-art by Brandon M. Greenwell, Bradley C. Boehmke Introduction to the vip Package Variable Importance Plots—An

Brandon M. Greenwell , Bradley C. Boehmke

CRAN packages used

iml, R6, foreach, ingredients, DALEX, mmpf, varImp, party, measures, vita, rfVarImpOOB, randomForestExplainer, tree.interpreter, pkgsearch, caret, mlr, ranger, vip, ggplot2, partykit, earth, nnet, vivo, pdp, microbenchmark, iBreakDown, fastshap, xgboost, ALEPlot, DT, mlr3, data.table, AmesHousing, SuperLearner, glmnet, kernlab, plyr, doParallel

CRAN Task Views implied by cited packages

MachineLearning, HighPerformanceComputing, Multivariate, Survival, Environmetrics, TeachingStatistics, Cluster, Econometrics, Finance, Graphics, ModelDeployment, NaturalLanguageProcessing, Optimization, Phylogenetics, ReproducibleResearch, SocialSciences, TimeSeries


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Greenwell & Boehmke, "The R Journal: Variable Importance Plots—An Introduction to the vip Package", The R Journal, 2020

BibTeX citation

  author = {Greenwell, Brandon M. and Boehmke, Bradley C.},
  title = {The R Journal: Variable Importance Plots—An Introduction to the vip Package},
  journal = {The R Journal},
  year = {2020},
  note = {},
  doi = {10.32614/RJ-2020-013},
  volume = {12},
  issue = {1},
  issn = {2073-4859},
  pages = {343-366}