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AcME – Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
[article]
2021
arXiv
pre-print
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME can be applied a posteriori to each regression or classification model. Not only does AcME compute feature
arXiv:2112.12635v1
fatcat:4rqdqf3k3fe4fkm3k77nplv3py