Package: shapr 0.2.2

Martin Jullum

shapr: Prediction Explanation with Dependence-Aware Shapley Values

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) <arxiv:1903.10464>, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values.

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

shapr_0.2.2.tar.gz
shapr_0.2.2.tar.gz(r-4.5-noble)shapr_0.2.2.tar.gz(r-4.4-noble)
shapr_0.2.2.tgz(r-4.4-emscripten)shapr_0.2.2.tgz(r-4.3-emscripten)
shapr.pdf |shapr.html
shapr/json (API)
NEWS

# Install 'shapr' in R:
install.packages('shapr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/norskregnesentral/shapr/issues

Pkgdown:https://norskregnesentral.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

openblascppopenmp

3.90 score 2 stars 2 packages 1.3k downloads 21 exports 9 dependencies

Last updated 2 years agofrom:1c846f0fd8. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 10 2024
R-4.5-linux-x86_64OKDec 10 2024

Exports:aicc_full_single_cppcheck_featurescorrection_matrix_cppcreate_ctreeexplainfeature_combinationsfeature_matrix_cppget_data_specsget_model_specshat_matrix_cppmahalanobis_distance_cppmake_dummiesmodel_checkerobservation_impute_cpppredict_modelprepare_datapreprocess_datarss_cppshaprupdate_dataweight_matrix_cpp

Dependencies:BHcondMVNormdata.tablelatticeMatrixmvnfastmvtnormRcppRcppArmadillo

shapr: Explaining individual machine learning predictions with Shapley values

Rendered fromunderstanding_shapr.Rmdusingknitr::rmarkdownon Dec 10 2024.

Last update: 2023-02-27
Started: 2020-09-03