Can you explain the contrast between bagging and boosting? And what effects do they have on bias and variance?

Very hard Difficulty

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ML Knowledge question

ML Knowledge questions test your theoretical understanding of machine learning concepts. Showcase your grasp of algorithms, model training techniques, feature engineering, and the mathematical foundations of artificial intelligence.

Here's a Hint

Compare how bagging reduces variance through parallel ensemble training, while boosting reduces bias through sequential, error-focused training, with concrete examples like Random Forest vs. AdaBoost.