shapper: Wrapper of Python Library 'shap'

Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <doi:10.48550/arXiv.1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.

Version:	0.1.3
Imports:	reticulate, DALEX, ggplot2
Suggests:	covr, knitr, randomForest, rpart, testthat, markdown, qpdf
Published:	2020-08-28
DOI:	10.32614/CRAN.package.shapper
Author:	Szymon Maksymiuk [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut], Mateusz Staniak [ctb], Michal Burdukiewicz [ctb]
Maintainer:	Szymon Maksymiuk <sz.maksymiuk at gmail.com>
BugReports:	https://github.com/ModelOriented/shapper/issues
License:	GPL-2 | GPL-3 [expanded from: GPL]
URL:	https://github.com/ModelOriented/shapper
NeedsCompilation:	no
Materials:	NEWS
In views:	MachineLearning
CRAN checks:	shapper results

Documentation:

Reference manual:	shapper.pdf
Vignettes:	How to use shapper for classification How to use shapper for regression

Downloads:

Package source:	shapper_0.1.3.tar.gz
Windows binaries:	r-devel: shapper_0.1.3.zip, r-release: shapper_0.1.3.zip, r-oldrel: shapper_0.1.3.zip
macOS binaries:	r-release (arm64): shapper_0.1.3.tgz, r-oldrel (arm64): shapper_0.1.3.tgz, r-release (x86_64): shapper_0.1.3.tgz, r-oldrel (x86_64): shapper_0.1.3.tgz
Old sources:	shapper archive

Linking:

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