Practice Pruning And Overfitting (3.6.3) - Kernel & Non-Parametric Methods
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Pruning and Overfitting

Practice - Pruning and Overfitting

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define overfitting in your own words.

💡 Hint: Think about how a student might memorize answers without understanding.

Question 2 Easy

What is pruning?

💡 Hint: Consider how gardeners trim plants.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is overfitting?

A method of simplifying models
A modeling error capturing noise
A type of tree structure

💡 Hint: Think about the consequence of too much learning from data.

Question 2

True or False: Pruning can lead to a decrease in training accuracy.

True
False

💡 Hint: Remember, pruning reduces complexity.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a dataset where you notice your decision tree is performing excellently on training data but poorly on validation data. How would you approach pruning, and what steps would you take?

💡 Hint: Consult performance metrics to inform your pruning decisions.

Challenge 2 Hard

Imagine you have a decision tree with many splits leading to small, specific rules to classify data. How can pre-pruning aid in improving the model's generalization?

💡 Hint: Think about how stopping growth early can impact complexity.

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Reference links

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