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pro vyhledávání: '"Erkelens, Andrej"'
Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models. In this paper, we use Shapley Taylor interaction indices (STII) to analyze the impact of underlying data structure on
Externí odkaz:
http://arxiv.org/abs/2403.13106
Interpretable machine learning plays a key role in healthcare because it is challenging in understanding feature importance in deep learning model predictions. We propose a novel framework that uses deep learning to study feature sensitivity for mode
Externí odkaz:
http://arxiv.org/abs/2210.03258