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Explain the concept of model selection in ensemble learning. How do bagging, boosting, and stacking determine the optimal base models for their ensembles?
A
Bagging selects models based on variance reduction, boosting selects models based on bias reduction, and stacking combines models based on predictive performance.
B
Model selection is not relevant in ensemble methods. All methods use the same base models.
C
Model selection is identical in all ensemble methods. All methods use random selection of base models.
D
Model selection is only relevant for stacking. Bagging and boosting do not consider model selection.
