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.

Package details

AuthorNikolai Sellereite [aut] (<>), Martin Jullum [cre, aut] (<>), Annabelle Redelmeier [aut], Anders Løland [ctb], Jens Christian Wahl [ctb], Camilla Lingjærde [ctb], Norsk Regnesentral [cph, fnd]
MaintainerMartin Jullum <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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shapr documentation built on Jan. 28, 2021, 5:06 p.m.