EFI swMATH ID: 43040 Software Authors: Aayush Kumar, Jimiama Mafeni Mase, Divish Rengasamy, Benjamin Rothwell, Mercedes Torres Torres, David A. Winkler, Grazziela P. Figueredo Description: EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python. 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. Homepage: https://arxiv.org/abs/2208.04343 Dependencies: Python Keywords: EFI Toolbox; Ensemble Feature Importance; Python; Machine Learning; arXiv_cs.LG; Logic; arXiv_cs.LO; Feature Importance; Fuzzy Logic; Decision Fusion; Interpretability; Responsible AI Related Software: Espresso; NIMEFI; InterpretML; Alibi Explain; auto-sklearn; dalex; AI Explainability 360; XAI; Python Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python Aayush Kumar, Jimiama Mafeni Mase, Divish Rengasamy, Benjamin Rothwell, Mercedes Torres Torres, David A. Winkler, Grazziela P. Figueredo 2022