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) , partial dependence plots described by Friedman (2001) <>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models.


Reference manual

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0.5.1 by Christoph Molnar, a month ago

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Authors: Christoph Molnar [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table

Suggests randomForest, gower, testthat, rpart, MASS, caret, e1071, lime, mlr, covr, knitr, rmarkdown

See at CRAN