Stacking
Stacking, also known as stacked generalization, is an ensemble machine learning technique that combines multiple base models (called level-0 models) by training a meta-model (called level-1 model) to make final predictions based on their outputs. It works by using the predictions of the base models as input features for the meta-model, which learns to optimally combine them to improve overall accuracy and robustness. This approach often outperforms individual models by leveraging their diverse strengths and reducing overfitting.
Developers should learn stacking when building high-performance predictive models in machine learning competitions or production systems where accuracy is critical, such as in finance for credit scoring, healthcare for disease diagnosis, or e-commerce for recommendation engines. It is particularly useful when dealing with complex datasets where no single model performs best, as it can capture different patterns and reduce variance through model diversity.