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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does LightGBM stand for?
💡 Hint: Think about its focus on speed and efficiency.
Question 2
Easy
What type of data is CatBoost optimized for?
💡 Hint: Remember, it directly handles a certain type of feature.
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 type of tree growth does LightGBM use?
- Level-wise
- Leaf-wise
- Depth-wise
💡 Hint: Think about which approach is better for capturing complexities.
Question 2
True or False: CatBoost requires categorical features to be manually encoded before modeling.
- True
- False
💡 Hint: Remember its core advantage.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset with millions of records, significantly containing categorical features. Which algorithm would you leverage and why? Elaborate on your choice comparing LightGBM and CatBoost.
💡 Hint: Think about what it takes to preprocess and the strengths of both algorithms.
Question 2
If tasked with improving a current model that is overfitting, what strategies could be derived from CatBoost's methods that could also be applied to other models?
💡 Hint: Consider how controlled learning and validation might help.
Challenge and get performance evaluation