• Corpus ID: 220495856

Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies

@article{Fryer2020ExplainingTD,
  title={Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies},
  author={Daniel Vidali Fryer and Inga Str{\"u}mke and Hien Nguyen},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.06011}
}
Shapley values have become increasingly popular in the machine learning literature thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain notions of `fairness'. The flexibility arises from the myriad potential forms of the Shapley value \textit{game formulation}. Amongst the consequences of this flexibility is that there are now many types of Shapley values being discussed, with such variety being a source of potential misunderstanding. To the best of our… 

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