Regression Framework

Regression frameworks aim to model the relationship between variables by estimating a function that maps inputs to outputs, focusing on minimizing prediction error and enhancing model interpretability and stability. Current research emphasizes advancements in model architectures, including the application of large language models, graph-based approaches for handling complex data structures, and partially-interpretable neural networks for improved accuracy and interpretability in diverse applications. These improvements address challenges such as imbalanced datasets, high-dimensional data, and the need for efficient and robust methods across various domains, from industrial signal processing to environmental risk assessment. The resulting advancements have significant implications for diverse fields, improving prediction accuracy and enabling better decision-making in areas like predictive maintenance and risk management.