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] (<https://orcid.org/0000-0002-4671-0337>), Martin Jullum [cre, aut] (<https://orcid.org/0000-0003-3908-5155>), Annabelle Redelmeier [aut], Anders Løland [ctb], Jens Christian Wahl [ctb], Camilla Lingjærde [ctb], Norsk Regnesentral [cph, fnd]
MaintainerMartin Jullum <Martin.Jullum@nr.no>
LicenseMIT + file LICENSE
Version0.2.0
URL https://norskregnesentral.github.io/shapr/ https://github.com/NorskRegnesentral/shapr
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("shapr")

Try the shapr package in your browser

Any scripts or data that you put into this service are public.

shapr documentation built on Jan. 28, 2021, 5:06 p.m.