juliafried/imlplots: Interactive Plots for Interpretable Machine Learning

Make the predictions of machine learning models explainable by opening up the blackboxes of numerous non-linear statistical models. This package provides a user-friendly interactive interface to produce Individual Conditional Expectation (ICE) plots, Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots.

Getting started

Package details

Authorc( person("Julia", "Fried", role = c("aut", "cre")), person("Tobias", "Riebe", role = c("aut", "cre")), person("Christian", "Scholbeck", email = "[email protected]", role = c("aut", "cre")))
Maintainer
LicenseBSD_2_clause
Version0.1.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("juliafried/imlplots")
juliafried/imlplots documentation built on Sept. 26, 2018, 10:05 a.m.