Efficient implementation of Kernel SHAP, see Lundberg and Lee (2017), and Covert and Lee (2021) <http://proceedings.mlr.press/v130/covert21a>. Furthermore, for up to 14 features, exact permutation SHAP values can be calculated. The package plays well together with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'. Visualizations can be done using the R package 'shapviz'.
Version: | 0.7.0 |
Depends: | R (≥ 3.2.0) |
Imports: | foreach, MASS, stats, utils |
Suggests: | doFuture, testthat (≥ 3.0.0) |
Published: | 2024-08-17 |
DOI: | 10.32614/CRAN.package.kernelshap |
Author: | Michael Mayer |
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 |
Reference manual: | kernelshap.pdf |
Package source: | kernelshap_0.7.0.tar.gz |
Windows binaries: | r-devel: kernelshap_0.7.0.zip, r-release: kernelshap_0.7.0.zip, r-oldrel: kernelshap_0.7.0.zip |
macOS binaries: | r-release (arm64): kernelshap_0.7.0.tgz, r-oldrel (arm64): kernelshap_0.7.0.tgz, r-release (x86_64): kernelshap_0.7.0.tgz, r-oldrel (x86_64): kernelshap_0.7.0.tgz |
Old sources: | kernelshap archive |
Reverse imports: | survex |
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