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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Bagging stand for?
💡 Hint: Think about the sampling technique used in this method.
Question 2
Easy
How do out-of-bag samples function within Bagging?
💡 Hint: Consider how these samples are beneficial for assessing the model.
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 is the purpose of Bagging?
- To reduce bias
- To reduce variance
- To increase complexity
💡 Hint: Remember the main goal described during the lesson.
Question 2
True or False: Bagging trains multiple models on the same dataset.
- True
- False
💡 Hint: Think about how many different subsets are used in running Bagging.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are presented with a dataset containing considerable noise that adversely affects the prediction accuracy of individual models. Explain how you would implement Bagging to improve prediction outcomes.
💡 Hint: Think about how diversity among models can mitigate the effects of inconsistent data.
Question 2
Discuss the potential trade-offs between using Bagging and Boosting on a dataset with a significant number of features but few instances. Which method would you choose and why?
💡 Hint: Consider how each method deals with the challenges presented by complex datasets.
Challenge and get performance evaluation