pdp: A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

Source: R/pdp.R

Partial dependence plots (PDPs) help visualize the relationship between a subset of the features (typically 1-3) and the response while accounting for the average effect of the other predictors in the model. They are particularly effective with black box models like random forests and support vector machines.

Details

The development version can be found on GitHub: https://github.com/bgreenwell/pdp. As of right now, pdp exports four functions:

  • partial - construct partial dependence functions (i.e., objects of class "partial") from various fitted model objects;

  • plotPartial - plot partial dependence functions (i.e., objects of class "partial") using lattice graphics;

  • autoplot - plot partial dependence functions (i.e., objects of class "partial") using ggplot2 graphics;

  • topPredictors - extract most "important" predictors from various types of fitted models.