kernelshap: Kernel SHAP
Efficient implementation of Kernel SHAP, see Lundberg and Lee (2017) <https://dl.acm.org/doi/10.5555/3295222.3295230>, and Covert and Lee (2021) <http://proceedings.mlr.press/v130/covert21a>. For models with up to eight features, the results are exact regarding the selected background data. Otherwise, an almost exact hybrid algorithm involving iterative sampling is used. The package plays well together with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'. Visualizations can be done using the R package 'shapviz'.
| Version: | 0.3.7 |
| Depends: | R (≥ 3.2.0) |
| Imports: | foreach, stats, utils |
| Suggests: | doFuture, testthat (≥ 3.0.0) |
| Published: | 2023-05-17 |
| Author: | Michael Mayer [aut, cre],
David Watson [aut],
Przemyslaw Biecek 
[ctb] |
| Maintainer: | Michael Mayer <mayermichael79 at gmail.com> |
| BugReports: | https://github.com/ModelOriented/kernelshap/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ModelOriented/kernelshap |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | MachineLearning |
| CRAN checks: | kernelshap results |
Documentation:
| Reference manual: | kernelshap.pdf |
Downloads:
| Package source: | kernelshap_0.3.7.tar.gz |
| Windows binaries: | r-devel: kernelshap_0.3.7.zip, r-release: kernelshap_0.3.7.zip, r-oldrel: kernelshap_0.3.7.zip |
| macOS binaries: | r-release (arm64): kernelshap_0.3.7.tgz, r-oldrel (arm64): kernelshap_0.3.7.tgz, r-release (x86_64): kernelshap_0.3.7.tgz, r-oldrel (x86_64): kernelshap_0.3.7.tgz |
| Old sources: | kernelshap archive |
Reverse dependencies:
| Reverse imports: | survex |
Linking:
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