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feature importance

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papersTODAY 04:00 UTC

Shapley Value Estimators Adapted for Multi-Site Data with Missing Features

A new arXiv paper addresses a limitation in population-level Shapley value estimation, a common approach for attributing feature importance in machine learning models. Existing estimators typically require fully observed data when evaluating the coalitional game, which fails when features are missing in blocks across multiple data sites. The work proposes estimation methods suited to this blockwise-missing, multi-site setting.

shapley-values/explainability/missing-data/research