Discuss the impact of model complexity on ensemble learning. How does the complexity of base models affect the performance of bagging, boosting, and stacking?

Simulated

Complex base models improve performance in bagging by reducing variance, in boosting by reducing bias, and in stacking by combining diverse predictions.

32.1%

Complex base models increase model complexity and reduce performance in all ensemble methods.

39.6%

Complex base models are not relevant for ensemble methods. All methods rely on simple, low-complexity models.

12.3%

Complex base models are only relevant for stacking. Bagging and boosting do not benefit from complex base models.

16.0%

Databricks Certified Machine Learning - Associate

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Discuss the impact of model complexity on ensemble learning. How does the complexity of base models affect the performance of bagging, boosting, and stacking?