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- W4383472600 abstract "Despite their ubiquitous use, Shapley value feature attributions can be misleading due to feature interaction in both model and data. We propose an alternative attribution approach, Shapley Sets, which awards value to sets of features. Shapley Sets decomposes the underlying model into non-separable variable groups using a recursive function decomposition algorithm with log linear complexity in the number of variables. Shapley Sets attributes to each non-separable variable group their combined value for a particular prediction. We show that Shapley Sets is equivalent to the Shapley value over the transformed feature set and thus benefits from the same axioms of fairness. Shapley Sets is value function agnostic and we show theoretically and experimentally how Shapley Sets avoids pitfalls associated with Shapley value based alternatives and are particularly advantageous for data types with complex dependency structure." @default.
- W4383472600 created "2023-07-07" @default.
- W4383472600 creator A5057936560 @default.
- W4383472600 creator A5092418511 @default.
- W4383472600 date "2023-07-04" @default.
- W4383472600 modified "2023-09-25" @default.
- W4383472600 title "Shapley Sets: Feature Attribution via Recursive Function Decomposition" @default.
- W4383472600 doi "https://doi.org/10.48550/arxiv.2307.01777" @default.
- W4383472600 hasPublicationYear "2023" @default.
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