Practice Lab: Applying and Comparing Regularization Techniques with Cross-Validation - 4 | 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

Define overfitting in your own words.

πŸ’‘ Hint: Think about how models memorize the training set instead of learning patterns.

Question 2

Easy

What is K-Fold cross-validation?

πŸ’‘ Hint: Consider the mechanism of creating multiple training and validation sets.

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 main purpose of regularization?

  • To minimize training error
  • To prevent overfitting
  • To speed up training

πŸ’‘ Hint: Consider what affects a model's ability to generalize.

Question 2

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

  • True
  • False

πŸ’‘ Hint: Think about how each regularization technique works.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You find that a model with Lasso regression has a high training error but performs well on the test set. Why might this happen?

πŸ’‘ Hint: Consider how feature selection impacts the training phase.

Question 2

If you applied both L1 and L2 regularization to the same model, what would the expected outcome be?

πŸ’‘ Hint: Think about what combining penalties would achieve.

Challenge and get performance evaluation