Practice Cross-Validation and Model Selection - 1.11 | 1. Learning Theory & Generalization | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is cross-validation?

πŸ’‘ Hint: Think about why we need to evaluate models differently.

Question 2

Easy

Describe K-Fold cross-validation.

πŸ’‘ Hint: How many times are we training with K-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 primary purpose of cross-validation?

  • To prevent overfitting
  • To increase model complexity
  • To train faster

πŸ’‘ Hint: Consider why we need to estimate model performance better.

Question 2

True or False: Leave-One-Out cross-validation is always preferable to K-Fold cross-validation.

  • True
  • False

πŸ’‘ Hint: Consider the trade-offs of computation vs. training accuracy.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset of 10,000 samples with an imbalanced class distribution of 90% to 10%, how would you set up a stratified K-Fold cross-validation?

πŸ’‘ Hint: Think about the ratio of classes in each fold.

Question 2

Analyze the potential impact of using plain K-Fold cross-validation versus stratified K-Fold on a highly imbalanced dataset.

πŸ’‘ Hint: Consider how the class ratios affect predictive modeling.

Challenge and get performance evaluation