Practice - L1 or L2 penalties
Practice Questions
Test your understanding with targeted questions
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.
Which penalty is associated with increasing sparsity in a model?
💡 Hint: Recall the term that relates to zeroing out coefficients.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does L1 regularization do?
💡 Hint: Remember the primary effect L1 has on the features.
True or False: L2 regularization completely removes features from the model.
💡 Hint: Consider whether any coefficients are forced to zero.
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.