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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0.1.3 by Martin Jullum, 3 months ago,

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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