pdp: An R Package for Constructing Partial Dependence Plots

Complex nonparametric models—like neural networks, random forests, and support vector machines—are more common than ever in predictive analytics, especially when dealing with large observational databases that don’t adhere to the strict assumptions imposed by traditional statistical techniques (e.g., multiple linear regression which assumes linearity, homoscedasticity, and normality). Unfortunately, it can be challenging to understand the results of such models and explain them to management. Partial dependence plots offer a simple solution. Partial dependence plots are low dimensional graphical renderings of the prediction function so that the relationship between the outcome and predictors of interest can be more easily understood. These plots are especially useful in explaining the output from black box models. In this paper, we introduce pdp, a general R package for constructing partial dependence plots.

Brandon M. Greenwell

CRAN packages used

randomForest, gbm, party, partykit, pdp, plotmo, lattice, ICEbox, car, effects, ggplot2, grid, latticeExtra, gridExtra, nnet, C50, rpart, adabag, ipred, adabag, xgboost, Cubist, MASS, earth, mda, ranger, e1071, kernlab, caret, magrittr, foreach, viridis, plyr, doMC, doParallel, dplyr

CRAN Task Views implied by cited packages

MachineLearning, Multivariate, Environmetrics, Survival, Econometrics, SocialSciences, Graphics, HighPerformanceComputing, Cluster, Distributions, Psychometrics, Finance, NaturalLanguageProcessing, NumericalMathematics, Optimization, Phylogenetics, Robust, WebTechnologies


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For attribution, please cite this work as

Greenwell, "The R Journal: pdp: An R Package for Constructing Partial Dependence Plots", The R Journal, 2017

BibTeX citation

  author = {Greenwell, Brandon M.},
  title = {The R Journal: pdp: An R Package for Constructing Partial Dependence Plots},
  journal = {The R Journal},
  year = {2017},
  note = {https://doi.org/10.32614/RJ-2017-016},
  doi = {10.32614/RJ-2017-016},
  volume = {9},
  issue = {1},
  issn = {2073-4859},
  pages = {421-436}