Stacking is an ensemble learning technique that combines multiple predictive models to produce a single, stronger model. This method involves training a new model, often called a meta-model, on the predictions made by the base models to improve overall accuracy and performance. By leveraging the strengths of various algorithms, stacking aims to reduce errors and enhance generalization on unseen data.
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