Empowering Machine Learning with Scalable Feature Engineering and Interpretable AutoML
Hassan Eldeeb,
Radwa Elshawi
Abstract:Automated feature engineering has gained considerable attention in academia and industry. Nevertheless, existing systems often lack practical scalability and efficiency. This paper introduces BigFeat, a scalable and interpretable framework that streamlines critical phases of the machine learning pipeline: feature engineering, model selection, and hyperparameter tuning. BigFeat presents two execution options: as a standalone feature engineering framework, denoted as BigFeat-FE, and as an AutoML framework, refer… Show more
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