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) , accumulated local effects plots described by Apley (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

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.7.0 by Christoph Molnar, 9 days ago

Report a bug at

Browse source code at

Authors: Christoph Molnar [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

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

Suggests randomForest, gower, testthat, rpart, MASS, caret, e1071, knitr, mlr, covr, rmarkdown, devtools, doParallel, ALEPlot, ranger

See at CRAN