Practice Practical Tips (7.7) - Ensemble Methods – Bagging, Boosting, and Stacking

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10 questions

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

Question 1.
What is Bagging used for?
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Think about what high variance means.
Question 2.
Give an example of when to use Boosting.
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What situations demand accuracy?
3Question 3.
Which method reduces variance by averaging predictions?
- Boosting
- Bagging
- Stacking
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Consider what 'averaging' means in the context of ensemble models.
4Question 4.
True or False: Boosting can lead to overfitting.
- True
- False
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Is it possible for a model to learn noise from the data?
Question 5.
You have three models: a decision tree, a logistic regression, and a neural network. Describe how you would use stacking to improve prediction accuracy.
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Think about gathering outputs from models to form a new training dataset.
Question 6.
Discuss the trade-offs of using Boosting versus Bagging in a high-stakes context, such as predicting customer credit risk.
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Balance accuracy with stability when considering real-world implications.

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