A machine learning technique that combines multiple models to create a more accurate and robust prediction system. By aggregating the outputs of several algorithms, ensemble methods often outperform individual models. Common approaches include bagging, boosting, and stacking. This technique reduces overfitting and improves generalization, making it valuable for critical AI applications.
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