kernelshap: Kernel SHAP
Multidimensional refinement of the Kernel SHAP algorithm
described in Ian Covert and Su-In Lee (2021)
<http://proceedings.mlr.press/v130/covert21a>. The package allows to
calculate Kernel SHAP values in an exact way, by iterative sampling
(as in the reference above), or by a hybrid of the two. As soon as
sampling is involved, the algorithm iterates until convergence, and
standard errors are provided. The package works with any model that
provides numeric predictions of dimension one or higher. Examples
include linear regression, logistic regression (on logit or
probability scale), other generalized linear models, generalized
additive models, and neural networks. The package plays well together
with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'.
Visualizations can be done using the R package 'shapviz'.
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