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Answer: Weak learners are simple models with limited predictive power. Bagging uses weak learners to reduce variance, boosting uses them to reduce bias, and stacking combines their predictions.
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.
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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?
A
Weak learners are simple models with limited predictive power. Bagging uses weak learners to reduce variance, boosting uses them to reduce bias, and stacking combines their predictions.
B
Weak learners are complex models with high predictive power. Bagging and boosting rely on weak learners to increase model complexity, while stacking reduces complexity.
C
Weak learners are not used in ensemble methods. All methods rely on strong, complex models.
D
Weak learners are identical to strong learners. All ensemble methods use weak learners interchangeably with strong learners.
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