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This paper presents an open-source Python toolbox called Ensemble FeatureImportance (EFI) to provide machine learning (ML) researchers, domain experts,and decision makers with robust and accurate feature importance quantificationand more reliable mechanistic interpretation of feature importance forprediction problems using fuzzy sets. The toolkit was developed to addressuncertainties in feature importance quantification and lack of trustworthyfeature importance interpretation due to the diverse availability of machinelearning algorithms, feature importance calculation methods, and datasetdependencies. EFI merges results from multiple machine learning models withdifferent feature importance calculation approaches using data bootstrappingand decision fusion techniques, such as mean, majority voting and fuzzy logic.The main attributes of the EFI toolbox are: (i) automatic optimisation of MLalgorithms, (ii) automatic computation of a set of feature importancecoefficients from optimised ML algorithms and feature importance calculationtechniques, (iii) automatic aggregation of importance coefficients usingmultiple decision fusion techniques, and (iv) fuzzy membership functions thatshow the importance of each feature to the prediction task. The key modules andfunctions of the toolbox are described, and a simple example of theirapplication is presented using the popular Iris dataset.

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