Practice Pruning and Overfitting - 3.6.3 | 3. Kernel & Non-Parametric Methods | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define overfitting in your own words.

πŸ’‘ Hint: Think about how a student might memorize answers without understanding.

Question 2

Easy

What is pruning?

πŸ’‘ Hint: Consider how gardeners trim plants.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is overfitting?

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

πŸ’‘ 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

πŸ’‘ Hint: Remember, pruning reduces complexity.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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?

πŸ’‘ Hint: Consult performance metrics to inform your pruning decisions.

Question 2

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?

πŸ’‘ Hint: Think about how stopping growth early can impact complexity.

Challenge and get performance evaluation