Practice - Implementing Lasso Regression with Cross-Validation
Practice Questions
Test your understanding with targeted questions
What is Lasso regression?
💡 Hint: Think about how it modifies the loss function.
Why is cross-validation important?
💡 Hint: Consider what could happen with just a single train/test split.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary goal of Lasso regression?
💡 Hint: Think about its impact on coefficients.
True or False: Lasso regression can shrink coefficients to exactly zero.
💡 Hint: Consider its unique L1 penalty.
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Challenge Problems
Push your limits with advanced challenges
You have a dataset with 100 features to predict housing prices. Describe how you would implement Lasso regression to determine which features are most influential, including the surrogate steps.
💡 Hint: Consider how each step contributes to feature selection.
Given an imbalanced dataset for classification, discuss how Stratified K-Fold cross-validation might improve your results compared to K-Fold in the context of a Lasso regression model.
💡 Hint: Reflect on the impact of class imbalance on model performance.
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Reference links
Supplementary resources to enhance your learning experience.