https://github.com/bgreenwell/pdp

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

black-box-model machine-learning partial-dependence-function partial-dependence-plot r visualization

Last synced: 7 days ago
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A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

• Host: GitHub
• URL: https://github.com/bgreenwell/pdp
• Owner: bgreenwell
• Created: 2016-03-09T01:29:34.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-07-18T08:14:48.000Z (over 2 years ago)
• Last Synced: 2024-10-29T21:23:33.181Z (9 days ago)
• Topics: black-box-model, machine-learning, partial-dependence-function, partial-dependence-plot, r, visualization
• Language: R
• Homepage: http://bgreenwell.github.io/pdp
• Size: 66.1 MB
• Stars: 92
• Watchers: 5
• Forks: 12
• Open Issues: 17
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output:

  md_document:

    variant: gfm

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  cache = TRUE,

  cache.path = "tools/README-cache/",

  collapse = TRUE,

  comment = "#>",

  fig.align = "center",

  fig.path = "man/figures/README-"

)

```

# pdp 

[![CRAN status](https://www.r-pkg.org/badges/version/investr)](https://CRAN.R-project.org/package=investr)

[![R-CMD-check](https://github.com/bgreenwell/pdp/workflows/R-CMD-check/badge.svg)](https://github.com/bgreenwell/pdp/actions)

[![Codecov test coverage](https://codecov.io/gh/bgreenwell/pdp/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bgreenwell/pdp?branch=master)

[![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/pdp)](https://cranlogs.r-pkg.org/badges/grand-total/pdp)

## Overview

[pdp](https://cran.r-project.org/package=pdp) is an R package for constructing _**p**artial **d**ependence **p**lots_ (PDPs) and _**i**ndividual **c**onditional **e**xpectation_ (ICE) curves. PDPs and ICE curves are part of a larger framework referred to as *interpretable machine learning* (IML), which also includes (but not limited to) _**v**ariable **i**mportance **p**lots_ (VIPs). While VIPs (available in the R package [vip](https://koalaverse.github.io/vip/index.html)) help visualize feature impact (either locally or globally), PDPs and ICE curves help visualize feature effects. An in-progress, but comprehensive, overview of IML can be found at the following URL: https://github.com/christophM/interpretable-ml-book.

A detailed introduction to [pdp](https://cran.r-project.org/package=pdp) has been published in The R Journal: "pdp: An R Package for Constructing Partial Dependence Plots", https://journal.r-project.org/archive/2017/RJ-2017-016/index.html. You can track development at https://github.com/bgreenwell/pdp. To report bugs or issues, contact the main author directly or submit them to https://github.com/bgreenwell/pdp/issues. For additional documentation and examples, visit the [package website](https://bgreenwell.github.io/pdp/index.html).

As of right now, `pdp` exports the following functions:

  * `partial()` - compute partial dependence functions and individual conditional expectations (i.e., objects of class `"partial"` and `"ice"`, respectively) from various fitted model objects;

  * `plotPartial()"` - construct `lattice`-based PDPs and ICE curves;

  * `autoplot()` - construct `ggplot2`-based PDPs and ICE curves;

  * ~~`topPredictors()` extract most "important" predictors from various types of fitted models.~~ see [vip](https://koalaverse.github.io/vip/index.html) instead for a more robust and flexible replacement;

  

  * `exemplar()` - construct an exemplar record from a data frame (**experimental** feature that may be useful for constructing fast, approximate feature effect plots.)

## Installation

```{r, eval=FALSE}

# The easiest way to get pdp is to install it from CRAN:

install.packages("pdp")

# Alternatively, you can install the development version from GitHub:

if (!("remotes" %in% installed.packages()[, "Package"])) {

  install.packages("remotes")

}

remotes::install_github("bgreenwell/pdp")

```