Stacking is an ensemble learning technique that combines multiple models to improve predictive performance. It involves training a new model, often called a meta-learner, to aggregate the predictions from several base models. This method leverages the strengths of different algorithms, enhancing accuracy and robustness by reducing the chances of overfitting and increasing generalization.
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