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Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define what is meant by 'Residual' in the context of GBM.
π‘ Hint: Think about what the model gets wrong in its predictions.
Question 2
Easy
What does the learning rate control in a GBM?
π‘ Hint: It affects how quickly or slowly the model learns.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does GBM stand for?
π‘ Hint: Think about which term emphasizes 'gradient'.
Question 2
True or False: GBM uses parallel learning.
π‘ Hint: Consider how each model learns from the previous one.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
Design a simple scenario where GBM would outperform a single decision tree. Provide reasoning for your answer.
π‘ Hint: Think about complexity in data relationships.
Question 2
How would you approach tuning the learning rate and the number of trees in GBM for a dataset with high variance?
π‘ Hint: Balance is key in tuning; reflect on the bias-variance trade-off.
Challenge and get performance evaluation