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4 cards from this section. Quick, good the night before a test.
What is the primary goal of ensemble methods?
Think about what combining models achieves.
Name one ensemble method.
List any technique discussed in class.
What are ensemble methods primarily used for?
Consider the purpose of combining multiple models.
True or False: Boosting can reduce both bias and variance.
Think about how boosting adjusts its focus with each iteration.
You are given a dataset with significant noise. Describe how you would utilize the ensemble methods discussed to achieve optimal performance.
Consider the benefits of each ensemble technique in relation to noise.
Discuss the importance of cross-validation in stacking and its potential impact on model performance.
Think about how cross-validation validates model reliability.
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