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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define overfitting and provide an example.
💡 Hint: Think about when a student remembers answers without understanding.
Question 2
Easy
What is the purpose of regularization in regression models?
💡 Hint: What do we want to avoid in model training?
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 overfitting?
- A model that captures only training data patterns
- A model that generalizes well
- A model with low training error and high test error
💡 Hint: Think of a student memorizing answers without understanding the content.
Question 2
True or False: Lasso regularization can set some coefficients to zero.
- True
- False
💡 Hint: Consider the difference in how Lasso and Ridge handle coefficients.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a comprehensive study evaluating the performance of Lasso, Ridge, and Elastic Net regression on a dataset of your choice. What metrics would you employ, and how would you compare model behaviors in terms of coefficient values?
💡 Hint: Focus on the interpretability of the coefficients alongside performance metrics.
Question 2
Consider a dataset with both categorical and numerical features. How would you process this data prior to applying regularization techniques? What challenges might arise?
💡 Hint: Reflect on the importance of preprocessing steps in model preparation.
Challenge and get performance evaluation