EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python

TL;DR

EFI is an open-source Python toolbox that combines multiple machine learning models and feature importance methods to provide more reliable and interpretable feature importance assessments for prediction tasks.

Contribution

The paper introduces EFI, a novel toolbox that fuses feature importance results from diverse models and methods using decision fusion and fuzzy logic for enhanced interpretability.

Findings

EFI improves robustness of feature importance quantification.

The toolbox automates model optimization and importance aggregation.

Fuzzy membership functions visualize feature relevance.

Abstract

This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with robust and accurate feature importance quantification and more reliable mechanistic interpretation of feature importance for prediction problems using fuzzy sets. The toolkit was developed to address uncertainties in feature importance quantification and lack of trustworthy feature importance interpretation due to the diverse availability of machine learning algorithms, feature importance calculation methods, and dataset dependencies. EFI merges results from multiple machine learning models with different feature importance calculation approaches using data bootstrapping and decision fusion techniques, such as mean, majority voting and fuzzy logic. The main attributes of the EFI toolbox are: (i) automatic optimisation of ML algorithms, (ii) automatic computation of a set of feature importance coefficients from optimised ML algorithms and feature importance calculation techniques, (iii) automatic aggregation of importance coefficients using multiple decision fusion techniques, and (iv) fuzzy membership functions that show the importance of each feature to the prediction task. The key modules and functions of the toolbox are described, and a simple example of their application is presented using the popular Iris dataset.