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:
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')) |
Bug tracker:https://github.com/norskregnesentral/shapr/issues
Pkgdown:https://norskregnesentral.github.io
Last updated 2 years agofrom:1c846f0fd8. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 10 2024 |
R-4.5-linux-x86_64 | OK | Dec 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
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Explain the output of machine learning models with more accurately estimated Shapley values | explain explain.combined explain.copula explain.ctree explain.ctree_comb_mincrit explain.empirical explain.gaussian |
Define feature combinations, and fetch additional information about each unique combination | feature_combinations |
Initiate the making of dummy variables | make_dummies |
Plot of the Shapley value explanations | plot.shapr |
Create an explainer object with Shapley weights for test data. | shapr |