Relative Feature Importance

TL;DR

The paper introduces Relative Feature Importance (RFI), a flexible method for assessing feature relevance in machine learning models relative to other feature subsets, extending existing importance measures like PFI and CFI.

Contribution

It proposes RFI, a novel importance measure that generalizes PFI and CFI, allowing for nuanced feature relevance analysis including features not available during training.

Findings

RFI provides a more detailed feature importance assessment.

Theoretical analysis clarifies interpretation of RFI.

Demonstrations show RFI's practical usefulness.

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.