What is the difference between bagging and boosting as ensemble techniques?
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Incorrect.
The correct answer is B) Bagging trains models independently in parallel on bootstrapped samples to reduce variance; boosting trains models sequentially, each focusing on correcting the errors of the previous ones, primarily reducing bias
Correct Answer
Bagging trains models independently in parallel on bootstrapped samples to reduce variance; boosting trains models sequentially, each focusing on correcting the errors of the previous ones, primarily reducing bias
Bagging (e.g., Random Forest) reduces variance by averaging independent models trained on bootstrapped subsets. Boosting (e.g., AdaBoost, Gradient Boosting, XGBoost) builds models sequentially, with each new model emphasizing the mistakes of the ensemble so far, typically reducing bias.