Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary

Abstract

Machine learning regression techniques have become essential for modeling complex industrial systems in the industry 4.0 era. This summary reviews key regression methods—from linear models to deep learning—and examines their applications across manufacturing, energy, chemical processing, and predictive maintenance. We discuss how different techniques balance accuracy, interpretability, and computational requirements, and highlight real-world implementations that have achieved substantial efficiency gains. The review also addresses critical challenges including data quality, model interpretability, and generalization, while exploring future directions such as physics-informed models, federated learning, and explainable AI.