Stacking Framework

Stacking frameworks combine predictions from multiple base models to improve overall performance in machine learning tasks. Current research focuses on optimizing stacking techniques for diverse applications, including image processing (e.g., handling blurry images in 3D microscopy), time series forecasting, and various types of regression and classification problems. This approach enhances model robustness, accuracy, and generalizability, particularly in scenarios with noisy data or complex relationships between variables, finding applications in fields ranging from electrochemistry to medical diagnosis and robotics.