Machine learning has witnessed significant advancements, and ensemble methods have played a pivotal role in enhancing predictive performance. Among these ensemble techniques, stacking stands out as a powerful approach that combines multiple models to achieve superior results. In this article, we will delve into the essence of stacking ensembles in machine learning, uncovering their significance, principles, and practical applications. Understanding Stacking Ensembles Stacking, also known as stacked generalization, is a machine learning technique that focuses on combining the predictions of multiple base models (or learners) through a meta-learner. The idea behind stacking is to leverage the diverse strengths of these base models to improve overall predictive accuracy. Stacking is often referred to as a "meta-algorithm" because it involves using a secondary model (the meta-learner) to learn how to combine the outputs of the base models effectively, enhancing machine learning t...