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) <http://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.

Version:	0.10.0
Imports:	checkmate, data.table, Formula, future, future.apply, ggplot2, gridExtra, Metrics, prediction, R6
Suggests:	ALEPlot, bench, caret, covr, e1071, future.callr, glmnet, gower, h2o, keras, knitr, MASS, mlr, mlr3, party, partykit, patchwork, randomForest, ranger, rmarkdown, rpart, testthat, yaImpute
Published:	2020-03-26
Author:	Christoph Molnar [aut, cre], Patrick Schratz [aut]
Maintainer:	Christoph Molnar <christoph.molnar at gmail.com>
BugReports:	https://github.com/christophM/iml/issues
License:	MIT + file LICENSE
URL:	https://github.com/christophM/iml
NeedsCompilation:	no
Citation:	iml citation info
Materials:	NEWS
CRAN checks:	iml results

Downloads:

Reverse dependencies:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=iml to link to this page.