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Explain the concept of weak learners in the context of ensemble learning. How do bagging, boosting, and stacking utilize weak learners to improve model performance?
Explain the concept of weak learners in the context of ensemble learning. How do bagging, boosting, and stacking utilize weak learners to improve model performance?
Simulated
Explanation:
Weak learners are simple models with limited predictive power. In bagging, weak learners are trained in parallel to reduce variance and improve stability. In boosting, weak learners are trained sequentially, with each subsequent model trying to correct errors made by the previous model, thereby reducing bias and improving performance. In stacking, weak learners are combined with other models to leverage their diverse predictions, potentially leading to better overall performance.