Relative Feature Importance

16 Jul 2020Gunnar KönigChristoph MolnarBernd BischlMoritz Grosse-Wentrup

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI)... (read more)

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