Machine learning has seen tremendous advancements in recent years, and ensemble learning techniques have played a pivotal role in achieving state-of-the-art performance in various tasks. One such powerful ensemble method is "Stacking," which goes beyond the traditional wisdom of using individual models in isolation. By skillfully blending the strengths of different models, stacking aims to create a more powerful and robust predictive model. In this article, we delve into the essence of stacking ensembles and explore how they can significantly improve the performance of machine learning systems. Understanding Stacking: Stacking, also known as stacked generalization, is a technique that involves combining the predictions of multiple base models to create a new, meta-level model. Unlike traditional ensemble methods like bagging and boosting, which focus on parallel model training, stacking utilizes a two-stage process to enhance prediction accuracy. Base Models: In machine lear...