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In the context of ensemble learning, explain the concept of bagging and how it differs from boosting and stacking. Provide a detailed example of a scenario where each of these techniques would be most appropriate.
A
Bagging is a technique where multiple models are trained on different subsets of the data, and the final prediction is made by averaging the predictions of all models. It is most suitable for reducing the variance of the model.
B
Boosting is a technique where multiple models are trained sequentially, with each model focusing on the errors made by the previous model. It is most suitable for reducing the bias of the model.
C
Stacking is a technique where multiple models are trained, and their predictions are used as input features for a meta-model. It is most suitable for combining the strengths of different models.
D
All of the above are correct.
