Grouped feature importance and combined features effect plot

نویسندگان

چکیده

Abstract Interpretable machine learning has become a very active area of research due to the rising popularity algorithms and their inherently challenging interpretability. Most work in this been focused on interpretation single features model. However, for researchers practitioners, it is often equally important quantify importance or visualize effect feature groups. To address gap, we provide comprehensive overview how existing model-agnostic techniques can be defined groups assess grouped importance, focusing permutation-based, refitting, Shapley-based methods. We also introduce an importance-based sequential procedure that identifies stable well-performing combination space. Furthermore, combined plot, which technique group based sparse, interpretable linear features. used simulation studies real data examples analyze, compare, discuss these

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ژورنال

عنوان ژورنال: Data Mining and Knowledge Discovery

سال: 2022

ISSN: ['1573-756X', '1384-5810']

DOI: https://doi.org/10.1007/s10618-022-00840-5