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Relative Feature Importance

MCML Authors

Link to Profile Bernd Bischl PI Matchmaking

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Moritz Grosse-Wentrup

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

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). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.

inproceedings

ICPR 2020

25th International Conference on Pattern Recognition. Virtual - Milano, Italy, Jan 10-15, 2021.

Authors

G. König • C. Molnar • B. Bischl • M. Grosse-Wentrup

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: KMB+20

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