7.3.3.3 - XGBoost (Extreme Gradient Boosting)
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Practice Questions
Test your understanding with targeted questions
What does XGBoost stand for?
💡 Hint: Think about boosting in machine learning.
Name one advantage of using XGBoost.
💡 Hint: Consider how it processes data.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does XGBoost optimize?
💡 Hint: Consider its handling of large datasets.
True or False: XGBoost can naturally handle missing values.
💡 Hint: Think about how it simplifies data preprocessing.
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Challenge Problems
Push your limits with advanced challenges
Discuss the trade-offs between using L1 and L2 regularization in tuning XGBoost's hyperparameters.
💡 Hint: Think about how each type of regularization impacts model complexity.
Create a dataset with missing values and validate how XGBoost performs compared to a traditional Imputation method.
💡 Hint: Focus on the variance in results between XGBoost and traditional methods.
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