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Explain the differences between bagging and boosting in terms of model training, prediction, and handling of overfitting. Provide examples of when each method would be preferred.
A
Bagging trains models in parallel and averages predictions, reducing variance. Boosting trains models sequentially, reducing bias. Bagging is preferred for stable models, while boosting is preferred for weak learners.
B
Bagging trains models sequentially and averages predictions, reducing bias. Boosting trains models in parallel, reducing variance. Bagging is preferred for weak learners, while boosting is preferred for stable models.
C
Bagging and boosting are identical in training and prediction methods. Both are used to reduce model complexity.
D
Bagging and boosting are not effective in handling overfitting. Both methods increase model complexity.
