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) <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: 0.1.1
Imports: reticulate, ggplot2
Suggests: covr, DALEX, knitr, randomForest, rpart, testthat, titanic, qpdf
Published: 2019-07-10
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
CRAN checks: shapper results

Downloads:

Reference manual: shapper.pdf
Vignettes: How to use shapper for classification
How to use shapper for regression
Package source: shapper_0.1.1.tar.gz
Windows binaries: r-devel: shapper_0.1.1.zip, r-release: shapper_0.1.1.zip, r-oldrel: shapper_0.1.1.zip
OS X binaries: r-release: shapper_0.1.1.tgz, r-oldrel: shapper_0.1.1.tgz
Old sources: shapper archive

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