Explain the concept of model selection in ensemble learning. How do bagging, boosting, and stacking determine the optimal base models for their ensembles?

Simulated

Bagging selects models based on variance reduction, boosting selects models based on bias reduction, and stacking combines models based on predictive performance.

87.8%

Model selection is not relevant in ensemble methods. All methods use the same base models.

2.0%

Model selection is identical in all ensemble methods. All methods use random selection of base models.

4.1%

Model selection is only relevant for stacking. Bagging and boosting do not consider model selection.

6.1%

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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?