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X-SHAP: towards multiplicative explainability of Machine Learning
[article]
2020
arXiv
pre-print
This paper introduces X-SHAP, a model-agnostic method that assesses multiplicative contributions of variables for both local and global predictions. This method theoretically and operationally extends the so-called additive SHAP approach. It proves useful underlying multiplicative interactions of factors, typically arising in sectors where Generalized Linear Models are traditionally used, such as in insurance or biology. We test the method on various datasets and propose a set of techniques
arXiv:2006.04574v2
fatcat:34wyu6am55fx3lz6pdhqe5dzua