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
 [aut],
Martin Jullum
[cre, aut],
Annabelle Redelmeier [aut],
Anders Løland [ctb],
Jens Christian Wahl [ctb],
Camilla Lingjærde [ctb],
Norsk Regnesentral [cph, fnd] |
| 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 |