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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.
