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- Version: 0.3.2
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- Date: 2022-12-17 09:49:19 UTC
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- SHA: 95de1c9c09c75c43ee0407dc62a63bdfa78e2430
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+ Version: 0.3.3
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+ Date: 2023-01-11 17:19:37 UTC
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+ SHA: 185fbe34878112883f33fb5bc7ee544442492f1d
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- # The "kernelshap" package <a href =' https://github.com/mayer79/kernelshap ' ><img src =' man/figures/logo.png ' align =" right " height =" 138.5 " /></a >
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+ # kernelshap <a href =' https://github.com/mayer79/kernelshap ' ><img src =' man/figures/logo.png ' align =" right " height =" 138.5 " /></a >
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+ [ ![ CRAN version] ( http://www.r-pkg.org/badges/version/kernelshap )] ( https://cran.r-project.org/package=kernelshap ) [ ![ ] ( https://cranlogs.r-pkg.org/badges/kernelshap )] ( https://cran.r-project.org/package=kernelshap ) [ ![ ] ( https://cranlogs.r-pkg.org/badges/grand-total/kernelshap?color=orange )] ( https://cran.r-project.org/package=kernelshap )
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## Introduction
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@@ -28,6 +31,17 @@ Additional arguments of `kernelshap()` can be used to control details of the alg
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- Meta-learner packages like "tidyvmodels", "caret", or "mlr3" are straightforward to use.
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- In order to use parallel processing, the backend must be set up beforehand, see the example below.
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+ ## Installation
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+ ``` r
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+ # From CRAN
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+ install.packages(" kernelshap" )
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+ # Or the newest version from GitHub:
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+ # install.packages("devtools")
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+ devtools :: install_github(" mayer79/kernelshap" )
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+ ```
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## Workflow to explain any model
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The typical workflow to explain any model with Kernel SHAP:
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Hello CRAN team
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- I am aware that the last package update is not too long ago. Still, I saw that
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- one of its dependencies (dorng) has status ORPHANED. While this might only be a
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+ I saw that a strong dependency ("dorng"") has status ORPHANED. While this might only be a
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temporary problem with "dorng", I have removed that package from the dependencies.
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