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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of scaling features before applying regularization?
💡 Hint: Think about how features with larger numerical ranges can dominate the model's training.
Question 2
Easy
Name one regularization technique used to reduce overfitting.
💡 Hint: Recall the techniques we discussed in the chapter.
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 does regularization aim to achieve in machine learning?
- Increase overfitting
- Improve model accuracy on unseen data
- Ignore noise in data
💡 Hint: Remember the purpose of regularization techniques.
Question 2
Is it true that Lasso regression can perform feature selection?
- True
- False
💡 Hint: Consider how Lasso's penalty works.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose you have two models: one with high variance resulting from overfitting, and another with high bias from underfitting. Describe how you would use regularization techniques to improve them.
💡 Hint: Think about both reducing complexity and increasing feature relevance.
Question 2
You implement Lasso regression and some coefficients become zero. Explain how this impacts model interpretation and future steps.
💡 Hint: Consider why feature selection might help in real-world applications.
Challenge and get performance evaluation