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DESCRIPTION
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DESCRIPTION
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Type: Package
Package: iml
Title: Interpretable Machine Learning
Version: 0.11.3
Authors@R: c(
person("Giuseppe", "Casalicchio", , "[email protected]", role = c("aut", "cre")),
person("Christoph", "Molnar", , "[email protected]", role = c("aut")),
person("Patrick", "Schratz", , "[email protected]", role = "aut",
comment = c(ORCID = "0000-0003-0748-6624"))
)
Maintainer: Giuseppe Casalicchio <[email protected]>
Description: 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.
License: MIT + file LICENSE
URL: https://giuseppec.github.io/iml/, https://github.com/giuseppec/iml/
BugReports: https://github.com/giuseppec/iml/issues
Imports:
checkmate,
data.table,
Formula,
future,
future.apply,
ggplot2,
Metrics,
R6
Suggests:
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
VignetteBuilder:
knitr
Config/testthat/edition: 3
Config/testthat/parallel: true
Encoding: UTF-8
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.2