Construction of the Theoretical Framework for the Generalization Ability of Machine Learning Models in the Big Data Environment

This article focuses on the construction of the theoretical framework for the generalization ability of machine learning models in the big data environment. First, deeply analyze the characteristics of the big data environment, elaborate on its multi-faceted impact on the generalization ability of machine learning models, and conduct a detailed analysis of the numerous challenges currently faced by the research on the generalization ability of machine learning models. Then, from the perspectives of enhancing model reliability, promoting application expansion and facilitating theoretical development, the necessity of constructing a theoretical framework for generalization ability is discussed. Then, comprehensively explore the constituent elements of the theoretical framework from multiple dimensions such as data, models, and algorithms, and construct a comprehensive theoretical framework model. Finally, the future research directions are prospected, aiming to provide comprehensive theoretical support for the improvement of the generalization ability of machine learning models in the big data environment.