Ensemble methods are machine learning techniques that combine the predictions from multiple individual models to make more accurate predictions than any single model alone. They are commonly used to improve the robustness and accuracy of machine learning algorithms, especially in tasks like classification and regression. Common ensemble methods include bagging, boosting, and stacking.
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