The Feature Importance Ranking Measure

TL;DR

The paper introduces FIRM, a method that analyzes complex learning models to identify important features while accounting for feature correlations, enhancing interpretability without sacrificing predictive accuracy.

Contribution

FIRM is a novel retrospective analysis technique that improves feature importance ranking by considering feature correlations, bridging the gap between complex models and interpretability.

Findings

FIRM effectively identifies relevant features even with noisy data.

FIRM outperforms standard feature weighting methods in interpretability.

Analytical and simulation results demonstrate FIRM's advantages.

Abstract

Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations.