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) , 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.">

iml: Interpretable Machine Learning (original) (raw)

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.

Version: 0.11.4
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
Published: 2025-02-24
DOI: 10.32614/CRAN.package.iml
Author: Giuseppe Casalicchio [aut, cre], Christoph Molnar [aut], Patrick Schratz ORCID iD [aut]
Maintainer: Giuseppe Casalicchio <giuseppe.casalicchio at lmu.de>
BugReports: https://github.com/giuseppec/iml/issues
License: MIT + file
URL: https://giuseppec.github.io/iml/,https://github.com/giuseppec/iml/
NeedsCompilation: no
Citation: iml citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: iml results

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