Regression Model

Regression modeling aims to establish relationships between variables, primarily predicting a dependent variable based on independent variables. Current research emphasizes improving model accuracy and interpretability, exploring techniques like symbolic regression, neural networks (including those guided by traditional regression), and regularized methods (e.g., ridge regression) to handle high-dimensional data and uncertainty. These advancements are impacting diverse fields, from materials science and environmental risk assessment to medical diagnostics and financial forecasting, by enabling more accurate predictions and deeper insights from complex datasets.