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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is overfitting?
💡 Hint: Think about the difference between learning and memorizing.
Question 2
Easy
What does L1 regularization do?
💡 Hint: Consider how it selects features.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does overfitting indicate about a model's performance?
- It performs well on training data but poorly on unseen data
- It performs poorly on both training and test data
- It performs equally well on both datasets
💡 Hint: Think about the difference between memorizing answers and understanding concepts.
Question 2
True or False: Regularization techniques can only be applied to linear regression models.
- True
- False
💡 Hint: Consider the diversity of machine learning algorithms.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are designing a model where you have a dataset with 100 features. How would you decide between using Lasso, Ridge, or Elastic Net regularization?
💡 Hint: Assess your feature's relevance and correlations.
Question 2
During a cross-validation process, your model's performance fluctuates greatly between folds. What steps would you take to stabilize these estimates?
💡 Hint: Think about your dataset size and distribution.
Challenge and get performance evaluation