The R Journal: article published in 2017, volume 9:1

pdp: An R Package for Constructing Partial Dependence Plots
Brandon M. Greenwell , The R Journal (2017) 9:1, pages 421-436.

Abstract 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.

Received: 2016-09-30; online 2017-05-10
CRAN packages: 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 CRAN packages: MachineLearning, Multivariate, Environmetrics, Survival, Econometrics, SocialSciences, Graphics, HighPerformanceComputing, Cluster, Distributions, Psychometrics, Finance, NaturalLanguageProcessing, NumericalMathematics, Optimization, Phylogenetics, Robust, WebTechnologies

This article is licensed under a Creative Commons Attribution 4.0 International license.

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