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.
Package details |
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Author | Giuseppe Casalicchio [aut, cre], Christoph Molnar [aut], Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>) |
Maintainer | Giuseppe Casalicchio <giuseppe.casalicchio@lmu.de> |
License | MIT + file LICENSE |
Version | 0.11.3 |
URL | https://giuseppec.github.io/iml/ https://github.com/giuseppec/iml/ |
Package repository | View on CRAN |
Installation |
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