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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does pre-pruning accomplish in decision trees?
💡 Hint: Think about the relationship between complexity and generalization.
Question 2
Easy
What is the purpose of the max_depth parameter?
💡 Hint: This is about controlling how tall the tree can grow.
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 does pre-pruning in decision trees primarily prevent?
- Overfitting
- Underfitting
- Boosting
💡 Hint: Look at the purpose of pruning techniques.
Question 2
Is the max_depth parameter more likely to help maintain simplicity than overfitting?
- True
- False
💡 Hint: Think about how depth impacts complexity.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Create a decision tree model for a dataset with many overlapping classes. Define your pre-pruning strategy.
💡 Hint: Focus on how many samples should reliably inform your splits.
Question 2
Analyze the effects of no pre-pruning on training versus test accuracy in a decision tree.
💡 Hint: Look for divergences in performance metrics.
Challenge and get performance evaluation