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.

Simulated

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.

92.7%

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.

7.3%

Bagging and boosting are identical in training and prediction methods. Both are used to reduce model complexity.

0.0%

Bagging and boosting are not effective in handling overfitting. Both methods increase model complexity.

0.0%

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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.