Interpretable Machine Learning R package iml version 0.11.4 CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley (2018) <doi:10.48550/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) <doi:10.48550/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. Software Imports: checkmate, data.table, Formula, future, future.apply, ggplot2, Metrics, R6 Suggests: ALEPlot, bench, bit64, caret, covr, e1071, future.callr, glmnet, gower, h2o, keras (>= 2.2.5.0), knitr, MASS, mlr, mlr3, party, partykit, patchwork, randomForest, ranger, rmarkdown, rpart, testthat, yaImpute Giuseppe Casalicchio <giuseppe.casalicchio@lmu.de> Comprehensive R Archive Network (CRAN) Giuseppe Casalicchio [aut, cre], Christoph Molnar [aut], Patrick Schratz [aut] (ORCID: <https://orcid.org/0000-0003-0748-6624>) MIT + file LICENSE (https://CRAN.R-project.org/package=iml/LICENSE) 2025-02-24 application/tgz https://CRAN.R-project.org/package=iml doi:10.32614/CRAN.package.iml