Practice Post-pruning (cost-complexity Pruning) (5.5.2) - Supervised Learning - Classification Fundamentals (Weeks 6)
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Post-pruning (Cost-Complexity Pruning)

Practice - Post-pruning (Cost-Complexity Pruning)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is post-pruning?

💡 Hint: Think about the purpose of enhancing model performance.

Question 2 Easy

What does overfitting refer to?

💡 Hint: Consider what happens when a model learns too many details.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of post-pruning?

To increase tree complexity
To remove unnecessary branches
To speed up training time

💡 Hint: Think about how to simplify a complex model.

Question 2

True or False: Overfitting occurs when a model is too simple.

True
False

💡 Hint: Remember what happens with too much detail.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a scenario where a Decision Tree has a total of 10 branches with varying importance, how would you determine which branches to prune without significantly losing predictive accuracy?

💡 Hint: Consider the relationship between branches and overall model performance.

Challenge 2 Hard

Describe the implications of using a small validation set in post-pruning. What risks does this pose?

💡 Hint: Think about why sample size matters in making statistical decisions.

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Reference links

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