Question: This week we are learning about bagging and boosting approached and why they perform better in predicting outcomes of interest than their counterparts e.g.

This week we are learning about bagging and boosting approached and why

This week we are learning about bagging and boosting approached and why they perform better in predicting outcomes of interest than their counterparts e.g. linear regression. Suppose we have a numerical outcome of interest. If you're to decide between using bagging/boosting methods versus statistical linear regression, which one would you choose and why? Expand on when you will prefer the other (i.e the one you didn't choose). What are some of the key characteristics that distinguishes a random forest model from a gradient boosting model?

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