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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the main advantage of LightGBM over XGBoost?
💡 Hint: Think about processing speed and dataset sizes.
Question 2
Easy
Which algorithm is designed specifically for handling categorical features?
💡 Hint: Recall the names of the algorithms discussed.
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 LightGBM primarily use to enhance speed and efficiency?
- Histogram-based splitting
- Depth-wise growth
- Random feature selection
💡 Hint: Focus on the speed enhancements of the algorithm.
Question 2
True or False: CatBoost requires significant preprocessing for categorical features.
- True
- False
💡 Hint: Think about the purpose of CatBoost in handling data.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a large dataset with millions of rows but only a few categorical features. Would you choose LightGBM or CatBoost? Justify your choice.
💡 Hint: Focus on the strengths of each algorithm regarding speed and data structure.
Question 2
Describe a scenario in a real-world application where CatBoost provides a distinct advantage over LightGBM.
💡 Hint: Consider the nature of the data and the benefits of streamlined preprocessing.
Challenge and get performance evaluation