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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define overfitting in the context of machine learning models.
💡 Hint: Think about the model's ability to generalize.
Question 2
Easy
What is Lasso regression known for?
💡 Hint: Remember the 'sparsity' concept.
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 is overfitting?
- The model performs well on unseen data
- The model memorizes training data
- The model cannot learn patterns
💡 Hint: Think about the differences in performance on training vs test datasets.
Question 2
True or False: Lasso regression forces some coefficients to zero.
- True
- False
💡 Hint: Recall Lasso's unique property.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are given a dataset with a large number of features, some of which are likely irrelevant. Explain how you would determine whether to use Lasso, Ridge, or Elastic Net regularization in your model. Justify your choice based on the characteristics of your dataset.
💡 Hint: Think about the nature of the features you are dealing with.
Question 2
Assess how cross-validation alters the evaluation of a model compared to a single train-test split. What are some key metrics you might observe that illustrate better performance reliability?
💡 Hint: Consider how repeated evaluations minimize errors that might stem from random data allocation.
Challenge and get performance evaluation