Practice Lightgbm And Catboost (6.7) - Ensemble & Boosting Methods - Advance Machine Learning
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LightGBM and CatBoost

Practice - LightGBM and CatBoost

Learning

Practice Questions

Test your understanding with targeted questions

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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

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Reference links

Supplementary resources to enhance your learning experience.