Practice Disadvantages (7.3.5) - Ensemble Methods – Bagging, Boosting, and Stacking

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8 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is one disadvantage of Bagging?
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Consider what Bagging primarily addresses.
Question 2.
Name a common problem associated with Boosting.
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Think about issues when tuning models.
3Question 3.
What is a disadvantage of Bagging?
- Reduces bias
- Increases computation time
- Improves predictions
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Think about how many models Bagging creates.
4Question 4.
True or False: Boosting is less prone to overfitting than Bagging.
- True
- False
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Reflect on how each method deals with errors.
Question 5.
Evaluate the effectiveness of Bagging in a high bias model versus a high variance model. What are the implications?
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Think about how each model type responds to ensemble techniques.
Question 6.
Propose strategies for mitigating overfitting in Boosting and provide rationale behind your strategies.
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Consider tuning parameters and validation use.

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