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authorPeter Lo <peterloleungyau@gmail.com>2020-06-28 16:01:18 +0800
committerRicardo Wurmus <rekado@elephly.net>2020-08-04 23:06:59 +0200
commit763453e18e7080894a61c6e8e83e81a6da21fa76 (patch)
tree2e93c42dac54f3949cf293df5b0637e0a388fc99 /gnu/packages/cran.scm
parenta74f85407403c62890be672ac4b6f4ceac15c4a0 (diff)
downloadguix-763453e18e7080894a61c6e8e83e81a6da21fa76.tar.gz
gnu: Add r-iml.
* gnu/packages/cran.scm (r-iml): New variable.

Co-authored-by: Ricardo Wurmus <rekado@elephly.net>
Signed-off-by: Ricardo Wurmus <rekado@elephly.net>
Diffstat (limited to 'gnu/packages/cran.scm')
-rw-r--r--gnu/packages/cran.scm46
1 files changed, 46 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index f371e5f973..098b4ec77e 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -22686,3 +22686,49 @@ regression, time series, binary classification, classification, and
 information retrieval problems.  It has zero dependencies and a consistent,
 simple interface for all functions.")
     (license license:bsd-3)))
+
+(define-public r-iml
+  (package
+    (name "r-iml")
+    (version "0.10.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (cran-uri "iml" version))
+       (sha256
+        (base32
+         "0xm3q42qahq798ilgg050df0mahhbdfd3fx3i7cpx606h38si0x7"))))
+    (properties `((upstream-name . "iml")))
+    (build-system r-build-system)
+    (propagated-inputs
+     `(("r-checkmate" ,r-checkmate)
+       ("r-data-table" ,r-data-table)
+       ("r-formula" ,r-formula)
+       ("r-future" ,r-future)
+       ("r-future-apply" ,r-future-apply)
+       ("r-ggplot2" ,r-ggplot2)
+       ("r-gridextra" ,r-gridextra)
+       ("r-metrics" ,r-metrics)
+       ("r-prediction" ,r-prediction)
+       ("r-r6" ,r-r6)))
+    (native-inputs `(("r-knitr" ,r-knitr)))
+    (home-page "https://github.com/christophM/iml")
+    (synopsis "Interpretable machine learning")
+    (description
+     "This package provides interpretability methods to analyze the behavior
+and predictions of any machine learning model.  Implemented methods are:
+
+@itemize
+@item Feature importance described by Fisher et al. (2018),
+@item accumulated local effects plots described by Apley (2018),
+@item partial dependence plots described by Friedman (2001),
+@item individual conditional expectation ('ice') plots described by Goldstein
+  et al. (2013) @url{https://doi.org/10.1080/10618600.2014.907095},
+@item local models (variant of 'lime') described by Ribeiro et. al (2016),
+@item the Shapley Value described by Strumbelj et. al (2014)
+  @url{https://doi.org/10.1007/s10115-013-0679-x},
+@item feature interactions described by Friedman et. al
+  @url{https://doi.org/10.1214/07-AOAS148} and tree surrogate models.
+@end itemize
+")
+    (license license:expat)))