Explain the difference between bagging and boosting ensemble models.

Explain the difference between bagging and boosting ensemble models.



Answer: Both methods are examples of ensemble methods that combine multiple models to create the final model. A bagging model (random forests) will create all of its component models without any information from the other models, and then work to aggregate them all together. Boosted models are created sequentially where it creates the first model then uses data from that model (usually the errors/residuals) to create the next model, and so on.

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