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Fast Derivation of Shapley based Feature Importances through Feature Extraction Methods for Nanoinformatics
2021
Machine Learning: Science and Technology
This work presents an alternative model-agnostic attribution method to compute feature importance rankings for high dimensional data requiring dimension reduction. We make use of Shapley values within the Shapley additive explanation framework to determine the importance values of each of the feature in the data set. We then demonstrate that it is possible to significantly reduce the computational complexity of ranking features in high dimensional spaces by first applying principal component
doi:10.1088/2632-2153/ac0167
fatcat:rxtab2y7kfa2fie2nmlrycd7um