Stacking

Stacking, or stacked generalization, is an ensemble learning technique used in machine learning to improve predictive performance. It involves training multiple models (often of different types) on the same dataset and then combining their predictions through a meta-learner. The main characteristics of stacking include its ability to leverage the strengths of various models, reduce overfitting through diverse model selection, and enhance accuracy by integrating different perspectives on the data. Common use cases for stacking include competitions in data science, where maximizing predictive accuracy is crucial, and situations where model diversity can lead to better generalization on unseen data.