Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
Why does a random forest typically outperform a single decision tree?
B is correct. Bagging many decorrelated trees and averaging their predictions cancels out individual trees' overfitting tendencies, lowering variance without raising bias much.
Question 02
What is the core idea behind gradient boosting (e.g. XGBoost)?
B is correct. Boosting builds trees one at a time, each focused on the mistakes the existing ensemble is still making, gradually reducing overall error.
Question 03
When would you reach for XGBoost over a random forest?
C is correct. Boosted trees often edge out random forests on tabular data when properly tuned, at the cost of being more sensitive to hyperparameters and slightly slower to train sequentially.