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


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install.packages("shapr")

0.1.3 by Martin Jullum, 17 days ago


https://norskregnesentral.github.io/shapr/, https://github.com/NorskRegnesentral/shapr


Report a bug at https://github.com/NorskRegnesentral/shapr/issues


Browse source code at https://github.com/cran/shapr


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


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports stats, data.table, Rcpp, condMVNorm, mvnfast, Matrix

Suggests ranger, xgboost, mgcv, testthat, knitr, rmarkdown, roxygen2, MASS, ggplot2, gbm

Linking to RcppArmadillo, Rcpp


See at CRAN