Databricks Certified Machine Learning - Associate
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You are working on a time series forecasting problem and have decided to use an ensemble of models. Which of the following ensemble techniques would be most suitable for this scenario, and explain why?
A
Bagging, as it reduces the variance of the model and is less prone to overfitting.
B
Boosting, as it focuses on the errors made by previous models and can handle noisy data.
C
Stacking, as it combines the strengths of different models and can handle complex patterns in time series data.
D
None of the above, as ensemble techniques are not suitable for time series forecasting.