Practice - Lab: Applying and Comparing Regularization Techniques with Cross-Validation
Practice Questions
Test your understanding with targeted questions
Define overfitting in your own words.
💡 Hint: Think about how models memorize the training set instead of learning patterns.
What is K-Fold cross-validation?
💡 Hint: Consider the mechanism of creating multiple training and validation sets.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the main purpose of regularization?
💡 Hint: Consider what affects a model's ability to generalize.
True or False: Lasso regression can reduce some coefficients to zero.
💡 Hint: Think about how each regularization technique works.
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Challenge Problems
Push your limits with advanced challenges
You find that a model with Lasso regression has a high training error but performs well on the test set. Why might this happen?
💡 Hint: Consider how feature selection impacts the training phase.
If you applied both L1 and L2 regularization to the same model, what would the expected outcome be?
💡 Hint: Think about what combining penalties would achieve.
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Reference links
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