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Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements the method described in Aas, Jullum and Løland (2019) <doi:10.48550/arXiv.1903.10464>, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values.
Version: | 0.2.2 |
Depends: | R (≥ 3.5.0) |
Imports: | stats, data.table, Rcpp (≥ 0.12.15), condMVNorm, mvnfast, Matrix |
LinkingTo: | RcppArmadillo, Rcpp |
Suggests: | ranger, xgboost, mgcv, testthat, knitr, rmarkdown, roxygen2, MASS, ggplot2, caret, gbm, party, partykit |
Published: | 2023-05-04 |
DOI: | 10.32614/CRAN.package.shapr |
Author: | Nikolai Sellereite
|
Maintainer: | Martin Jullum <Martin.Jullum at nr.no> |
BugReports: | https://github.com/NorskRegnesentral/shapr/issues |
License: | MIT + file LICENSE |
URL: | https://norskregnesentral.github.io/shapr/, https://github.com/NorskRegnesentral/shapr |
NeedsCompilation: | yes |
Language: | en-US |
Materials: | README NEWS |
In views: | MachineLearning |
CRAN checks: | shapr results |
Reference manual: | shapr.pdf |
Vignettes: |
'shapr': Explaining individual machine learning predictions with Shapley values |
Package source: | shapr_0.2.2.tar.gz |
Windows binaries: | r-devel: shapr_0.2.2.zip, r-release: shapr_0.2.2.zip, r-oldrel: shapr_0.2.2.zip |
macOS binaries: | r-release (arm64): shapr_0.2.2.tgz, r-oldrel (arm64): shapr_0.2.2.tgz, r-release (x86_64): shapr_0.2.2.tgz, r-oldrel (x86_64): shapr_0.2.2.tgz |
Old sources: | shapr archive |
Reverse imports: | PPtreeregViz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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