Databricks Certified Machine Learning - Associate
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Describe a scenario where boosting would be more suitable than bagging or stacking. Explain how boosting addresses the specific challenges in this scenario.
A
Boosting is suitable for scenarios with high variance, as it reduces variance by training models sequentially.
B
Boosting is suitable for scenarios with high bias, as it focuses on improving the performance of weak learners.
C
Boosting is suitable for scenarios with complex relationships, as it combines predictions from multiple models.
D
Boosting is not suitable for any scenario, as it increases model complexity.