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 | 
| Reference manual: | shapper.html , shapper.pdf | 
| Vignettes: | How to use shapper for classification (source, R code) How to use shapper for regression (source, R code) | 
| 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 | 
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