kernelshap: Kernel SHAP
Implementation of the model-agnostic Kernel SHAP algorithm by
Ian Covert and Su-In Lee (2021)
<http://proceedings.mlr.press/v130/covert21a>. Due to its iterative
nature, standard errors of the SHAP values are provided and
convergence is monitored. The package allows to work with any model
that provides numeric predictions. Examples include linear
regression, logistic regression (logit or probability scale), other
generalized linear models, generalized additive models, and neural
networks. The package plays well together with meta-learning packages
like 'caret' or 'mlr3'. Visualizations can be done using the R package
'shapviz'.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
stats, utils |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2022-08-12 |
| Author: |
Michael Mayer [aut, cre] |
| Maintainer: |
Michael Mayer <mayermichael79 at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
kernelshap results |
Documentation:
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