Interpretability methods to analyze the behavior
and predictions of any machine learning model.  Implemented methods
are: Feature importance described by Fisher et al. (2018)
<arXiv:1801.01489>, accumulated local effects plots described by Apley
(2018) <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) <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.
checkmate, data.table, Formula, future, future.apply, ggplot2,
keras (>= 2.2.5.0), Metrics, prediction, R6
ALEPlot, bench, caret, covr, e1071, future.callr, glmnet,
gower, h2o, knitr, MASS, mlr, mlr3, party, partykit, patchwork,
randomForest, ranger, rmarkdown, rpart, testthat, yaImpute

c(
person("Christoph", "Molnar", , "christoph.molnar@gmail.com", role = c("aut", "cre")),
person("Patrick", "Schratz", , "patrick.schratz@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-0748-6624"))
)
Interpretability methods to analyze the behavior and
predictions of any machine learning model.  Implemented methods are:
Feature importance described by Fisher et al. (2018)
<arXiv:1801.01489>, accumulated local effects plots described by Apley
(2018) <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) <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.
checkmate, data.table, Formula, future, future.apply, ggplot2,
Metrics, prediction, R6
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