Publication

Abstract : Optimization of regression models remains essential to enhance both model precision and level of stability as well as operational computational efficiency in datasets with large sizes and high noise levels. This paper examines several gradient-based optimization approaches: Batch Gradient Descent, Mini-Batch Gradient Descent, Stochastic Gradient Descent, RMSprop, Momentum, Adam, Nadam, Adagrad and Adadelta. The analysis studies how these methods perform regarding convergence speed and predictive precision and stability control. Two datasets are used in the study including agricultural crop yield prediction data alongside air quality data for environmental analysis. The experimental outcomes show that adaptive optimization methods including Adam and Nadam achieve the best combination of stability and speed due to their superior effectiveness. The research delivers important data that helps users choose optimization methods according to their dataset features and computational needs to develop better regression modeling for practical uses.