christophM/iml: Interpretable Machine Learning

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>, accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>, partial dependence plots described by Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

Getting started

Package details

MaintainerChristoph Molnar <christoph.molnar@gmail.com>
LicenseMIT + file LICENSE
Version0.10.1.9000
URL https://christophm.github.io/iml/ https://github.com/christophM/iml/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("christophM/iml")
christophM/iml documentation built on Nov. 21, 2020, 12:20 p.m.