Practice - Introduction to Cross-Validation: K-Fold and Stratified K-Fold
Practice Questions
Test your understanding with targeted questions
What is the purpose of cross-validation in machine learning?
💡 Hint: Think about how we can learn about a model's performance.
How does K-Fold cross-validation differ from a single train/test split?
💡 Hint: Consider the difference in the number of estimates we get.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary benefit of using cross-validation?
💡 Hint: Consider how cross-validation averages results.
True or False: K-Fold cross-validation can sometimes lead to biased performance evaluations.
💡 Hint: Think about how the dataset's characteristics can impact results.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
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