Practice - Regularization Techniques: L1 (Lasso), L2 (Ridge), Elastic Net
Practice Questions
Test your understanding with targeted questions
What is the main purpose of regularization in machine learning?
💡 Hint: Think about how a complex model can perform on training vs. test data.
What does Lasso do to some coefficients?
💡 Hint: Focus on how Lasso handles feature selection.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does regularization primarily aim to prevent in machine learning models?
💡 Hint: Consider why a model might perform poorly on new data.
True or False: Lasso regression can set some coefficients to zero.
💡 Hint: Think about the effect of the absolute value penalties.
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Challenge Problems
Push your limits with advanced challenges
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?
💡 Hint: Think about the importance of feature selection versus maintaining complexity.
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.
💡 Hint: Consider the benefits of feature selection in model accuracy and interpretability.
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Reference links
Supplementary resources to enhance your learning experience.