Practice Regularization Techniques: L1 (Lasso), L2 (Ridge), Elastic Net - 3.1.2 | Module 2: Supervised Learning - Regression & Regularization (Weeks 4) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main purpose of regularization in machine learning?

πŸ’‘ Hint: Think about how a complex model can perform on training vs. test data.

Question 2

Easy

What does Lasso do to some coefficients?

πŸ’‘ Hint: Focus on how Lasso handles feature selection.

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 primarily aim to prevent in machine learning models?

  • Overfitting
  • Underfitting
  • Variance Reduction

πŸ’‘ Hint: Consider why a model might perform poorly on new data.

Question 2

True or False: Lasso regression can set some coefficients to zero.

  • True
  • False

πŸ’‘ Hint: Think about the effect of the absolute value penalties.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a dataset with multiple features that contribute equally to the outcome. Discuss how applying L2 regularization might affect the model compared to L1. Which scenario yields a better predictive model?

πŸ’‘ Hint: Think about the importance of feature selection versus maintaining complexity.

Question 2

You have a dataset with correlated features and suspect that some variables are irrelevant. How would you approach modeling this data using both Lasso and Elastic Net? Discuss your tuning strategy and expected outcomes.

πŸ’‘ Hint: Consider the benefits of feature selection in model accuracy and interpretability.

Challenge and get performance evaluation