Practice K-Fold Cross-Validation - 12.3.B | 12. Model Evaluation and Validation | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is K-Fold Cross-Validation?

πŸ’‘ Hint: Think about how we divide the data.

Question 2

Easy

How many times does the model train in K-Fold Cross-Validation?

πŸ’‘ Hint: Consider what happens to each fold.

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 purpose of K-Fold Cross-Validation?

  • To reduce model complexity
  • To validate model performance
  • To eliminate bias completely

πŸ’‘ Hint: Consider what validation means in this context.

Question 2

T/F: In K-Fold Cross-Validation, the same dataset is used for both training and testing.

  • True
  • False

πŸ’‘ Hint: Think about how the folds are used.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset of 500 samples, determine the implications of using K-Fold Cross-Validation with k = 20 versus k = 5. Discuss trade-offs.

πŸ’‘ Hint: Contrast the sizes and diversity of training sets with different values of k.

Question 2

How would K-Fold Cross-Validation change if the dataset was highly imbalanced? Outline your approach to modify K-Fold for such cases.

πŸ’‘ Hint: Consider adjusting the way folds are created to account for class distributions.

Challenge and get performance evaluation