Comprehensive Benchmarking Analysis for Evaluating Effectiveness of Transfer Learning-based Feature Engineering in AutoML

This study conducts a comprehensive benchmarking analysis to evaluate the effectiveness of transfer learning-based feature engineering in Automated Machine Learning (AutoML) systems. The research compares traditional manual feature engineering, standard AutoML approaches, and transfer learning-enhanced AutoML across diverse data modalities, including images, text, and tabular data. Experimental evaluations were carried out using CIFAR-10, IMDB Reviews, and Adult Census Income datasets, focusing on assessing each approach in terms of model performance, training time, and resource utilization. The findings reveal that transfer learning-enhanced AutoML significantly reduces training time by up to 45% while improving model accuracy by up to 20%, particularly for image and text datasets. Furthermore, scenarios with high feature reuse rates demonstrated memory utilization improvements of up to 30%. These results underscore the substantial advantages of integrating transfer learning into AutoML systems for optimizing feature engineering processes.