Discuss the role of hyperparameter tuning in ensemble learning. How does hyperparameter tuning affect the performance of bagging, boosting, and stacking?

Simulated

Hyperparameter tuning improves performance in bagging by reducing variance, in boosting by reducing bias, and in stacking by balancing predictions.

87.2%

Hyperparameter tuning does not affect the performance of ensemble methods. All methods rely on default hyperparameters.

6.4%

Hyperparameter tuning increases model complexity and reduces performance in all ensemble methods.

4.3%

Hyperparameter tuning is only relevant for stacking. Bagging and boosting do not benefit from hyperparameter tuning.

2.1%

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