Practice - Boosting - 4.4
Practice Questions
Test your understanding with targeted questions
What is boosting in machine learning?
💡 Hint: Think about how individual models can work together.
Name one boosting algorithm.
💡 Hint: These algorithms focus on training weak models.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What technique does boosting primarily use to improve accuracy?
💡 Hint: Think about the order of training in boosting.
True or False: Boosting always uses deep models as its learners.
💡 Hint: Consider what a 'weak learner' entails.
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Challenge Problems
Push your limits with advanced challenges
Given a dataset with clear class imbalance, how would you approach the problem using boosting techniques? What steps would you take?
💡 Hint: Consider how the principles of boosting can address difficulties in data.
List the necessary parameters you would tune while implementing XGBoost for a regression problem and justify each choice.
💡 Hint: Reflect on the balance between bias and variance when tuning parameters.
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Reference links
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