shapr: An R-package for explaining machine learning models with dependence-aware Shapley values

R Python C++ Submitted 10 December 2019Published 05 February 2020
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Editor: @terrytangyuan (all papers)
Reviewers: @frycast (all reviews), @expectopatronum (all reviews)

Authors

Nikolai Sellereite (0000-0002-4671-0337), Martin Jullum (0000-0003-3908-5155)

Citation

Sellereite et al., (2019). shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. Journal of Open Source Software, 5(46), 2027, https://doi.org/10.21105/joss.02027

@article{Sellereite2019, doi = {10.21105/joss.02027}, url = {https://doi.org/10.21105/joss.02027}, year = {2019}, publisher = {The Open Journal}, volume = {5}, number = {46}, pages = {2027}, author = {Nikolai Sellereite and Martin Jullum}, title = {shapr: An R-package for explaining machine learning models with dependence-aware Shapley values}, journal = {Journal of Open Source Software} }
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explainable AI interpretable machine learning shapley values feature dependence

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ISSN 2475-9066