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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is cross-validation?
💡 Hint: Think of how we test knowledge before a big exam.
Question 2
Easy
What are hyperparameters?
💡 Hint: Consider parameters like learning rate or number of neighbors.
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 the primary purpose of cross-validation?
- To increase the dataset size
- To improve model performance on training data
- To assess model generalization on unseen data
💡 Hint: What might happen if we don't test our models on unseen data?
Question 2
True or False: k-fold cross-validation will always have an equal number of samples in each fold.
- True
- False
💡 Hint: Consider how data might get distributed.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a hypothetical scenario where cross-validation improves a model's performance. Explain the dataset characteristics and how you would implement cross-validation.
💡 Hint: Consider how diverse data characteristics can be tested.
Question 2
How might the choice of k in k-fold cross-validation influence the assessment of model performance?
💡 Hint: Think about how data splits affect training.
Challenge and get performance evaluation