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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the primary purpose of using L1 and L2 penalties in machine learning?
💡 Hint: Think about what happens when a model learns too well from its training data.
Question 2
Easy
Which penalty is associated with increasing sparsity in a model?
💡 Hint: Recall the term that relates to zeroing out coefficients.
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 L1 regularization do?
- Shrinks coefficients to zero
- Keeps all coefficients
- Increases complexity
💡 Hint: Remember the primary effect L1 has on the features.
Question 2
True or False: L2 regularization completely removes features from the model.
- True
- False
💡 Hint: Consider whether any coefficients are forced to zero.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose you have a dataset with 100 features, and after applying L1 regularization, you find that only 20 features remain with non-zero coefficients. Discuss the potential impacts on your model performance and interpretability.
💡 Hint: Consider both interpretability and the risk of losing important data.
Question 2
You are evaluating the performance of models using L1 and L2 penalties. Compare their effectiveness in terms of bias and variance trade-off, especially in the context of high-dimensional datasets.
💡 Hint: Think about how each penalty interacts with the model complexity.
Challenge and get performance evaluation