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: 0.1.0
Imports: reticulate, ggplot2
Suggests: covr, DALEX, knitr, randomForest, rpart, testthat, titanic
Published: 2019-03-02
Author: Alicja Gosiewska [aut, cre], Przemyslaw Biecek [aut], Michal Burdukiewicz [ctb]
Maintainer: Alicja Gosiewska <alicjagosiewska at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: shapper results


Reference manual: shapper.pdf
Vignettes: How to use shapper for classification
How to use shapper for regression
Package source: shapper_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: shapper_0.1.0.tgz, r-oldrel: shapper_0.1.0.tgz


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