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: 0.1.2
Imports: reticulate, ggplot2
Suggests: covr, DALEX, knitr, randomForest, rpart, testthat, qpdf
Published: 2019-08-02
Author: Szymon Maksymiuk [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut], Mateusz Staniak [ctb], Michal Burdukiewicz [ctb]
Maintainer: Szymon Maksymiuk <sz.maksymiuk at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Materials: NEWS
CRAN checks: shapper results


Reference manual: shapper.pdf
Vignettes: How to use shapper for classification
How to use shapper for regression
Package source: shapper_0.1.2.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: shapper_0.1.2.tgz, r-release: shapper_0.1.2.tgz, r-oldrel: shapper_0.1.2.tgz
Old sources: shapper archive


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