Databricks Certified Machine Learning - Associate
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Discuss the role of diversity in ensemble learning. How does diversity among base models affect the performance of bagging, boosting, and stacking?
A
Diversity among base models improves performance in bagging by reducing variance, in boosting by reducing bias, and in stacking by combining diverse predictions.
B
Diversity among base models does not affect the performance of ensemble methods. All methods rely on identical base models.
C
Diversity among base models increases model complexity and reduces performance in all ensemble methods.
D
Diversity among base models is only relevant for stacking. Bagging and boosting do not benefit from diverse base models.