Practice Disadvantages - 7.3.5 | 7. Ensemble Methods – Bagging, Boosting, and Stacking | Data Science Advance
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Disadvantages

7.3.5 - Disadvantages

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is one disadvantage of Bagging?

💡 Hint: Consider what Bagging primarily addresses.

Question 2 Easy

Name a common problem associated with Boosting.

💡 Hint: Think about issues when tuning models.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a disadvantage of Bagging?

Reduces bias
Increases computation time
Improves predictions

💡 Hint: Think about how many models Bagging creates.

Question 2

True or False: Boosting is less prone to overfitting than Bagging.

True
False

💡 Hint: Reflect on how each method deals with errors.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Evaluate the effectiveness of Bagging in a high bias model versus a high variance model. What are the implications?

💡 Hint: Think about how each model type responds to ensemble techniques.

Challenge 2 Hard

Propose strategies for mitigating overfitting in Boosting and provide rationale behind your strategies.

💡 Hint: Consider tuning parameters and validation use.

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