c(person(given = "Christoph", family = "Molnar",
role = c("aut", "cre"), email = "christoph.molnar@gmail.com"))
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>,
partial dependence plots described by Friedman (2001) <http://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> and
tree surrogate models.
R6, checkmate, dplyr, tidyr, ggplot2, partykit, glmnet,
Metrics, data.table
randomForest, gower, testthat, rpart, MASS, caret, e1071,
lime, mlr

R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table
randomForest, gower, testthat, rpart, MASS, caret, e1071,
lime, mlr, covr

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

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>,
partial dependence plots described by Friedman (2001) <http://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.

R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table,
foreach
randomForest, gower, testthat, rpart, MASS, caret, e1071,
knitr, lime, mlr, covr, rmarkdown, devtools, doParallel

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) <http://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.
R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table,
foreach, yaImpute
randomForest, gower, testthat, rpart, MASS, caret, e1071,
knitr, mlr, covr, rmarkdown, devtools, doParallel, ALEPlot,
ranger