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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of cross-validation in machine learning?
💡 Hint: Think about how we can learn about a model's performance.
Question 2
Easy
How does K-Fold cross-validation differ from a single train/test split?
💡 Hint: Consider the difference in the number of estimates we get.
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 benefit of using cross-validation?
- It saves time
- It provides a more reliable performance estimate
- It is easier to implement
💡 Hint: Consider how cross-validation averages results.
Question 2
True or False: K-Fold cross-validation can sometimes lead to biased performance evaluations.
- True
- False
💡 Hint: Think about how the dataset's characteristics can impact results.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset with 80% of instances belonging to Class A and 20% to Class B. If applying K-Fold cross-validation, what issues might arise without stratification?
💡 Hint: Consider the implications of class proportions.
Question 2
In a scenario with a multiclass dataset with 5 classes, devise a method to implement cross-validation without misrepresenting any class.
💡 Hint: Think about how many examples of each class you would need in each fold.
Challenge and get performance evaluation