Explanation:
Bagging involves training the model with many different random samples (with replacement). Random forests combine many decision trees, and boosting creates prediction models sequentially. AdaBoost is a boosting technique.
(Book 2, Module 26.2, LO 26.f)
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Question 94
A researcher is interested in improving his predictions by combining many different machine learning algorithms. Which of the following ensemble learning types relates to averaging predictors from repeated data samples?
A
Bagging.
B
Boosting.
C
Random forests.
D
AdaBoost.
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