5.5 - LightGBM and CatBoost
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Practice Questions
Test your understanding with targeted questions
What does LightGBM stand for?
💡 Hint: Think about its focus on speed and efficiency.
What type of data is CatBoost optimized for?
💡 Hint: Remember, it directly handles a certain type of feature.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What type of tree growth does LightGBM use?
💡 Hint: Think about which approach is better for capturing complexities.
True or False: CatBoost requires categorical features to be manually encoded before modeling.
💡 Hint: Remember its core advantage.
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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