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Answer: 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.
Bagging trains multiple models in parallel and averages their predictions, which helps in reducing variance and handling overfitting. This method is preferred for stable models that have low bias but high variance. Boosting, on the other hand, trains models sequentially, focusing on correcting errors made by previous models, which helps in reducing bias. Boosting is preferred for weak learners that have high bias but low variance.
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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.