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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=shapper to link to this page.
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