Practice - Activities
Practice Questions
Test your understanding with targeted questions
What is the purpose of scaling features before applying regularization?
💡 Hint: Think about how features with larger numerical ranges can dominate the model's training.
Name one regularization technique used to reduce overfitting.
💡 Hint: Recall the techniques we discussed in the chapter.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does regularization aim to achieve in machine learning?
💡 Hint: Remember the purpose of regularization techniques.
Is it true that Lasso regression can perform feature selection?
💡 Hint: Consider how Lasso's penalty works.
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Challenge Problems
Push your limits with advanced challenges
Suppose you have two models: one with high variance resulting from overfitting, and another with high bias from underfitting. Describe how you would use regularization techniques to improve them.
💡 Hint: Think about both reducing complexity and increasing feature relevance.
You implement Lasso regression and some coefficients become zero. Explain how this impacts model interpretation and future steps.
💡 Hint: Consider why feature selection might help in real-world applications.
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Reference links
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