Practice Cross-Validation - 6.3.3 | Machine Learning Basics | AI Course Fundamental
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define cross-validation in your own words.

πŸ’‘ Hint: Think about evaluating performance on unfamiliar datasets.

Question 2

Easy

What is the purpose of k-fold cross-validation?

πŸ’‘ Hint: Consider why we might want to see how the model performs across different samples.

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 cross-validation primarily used for?

  • To increase training dataset size
  • To evaluate model generalization
  • To optimize hyperparameters

πŸ’‘ Hint: Think about why we evaluate model performance.

Question 2

True or False: k-fold cross-validation can help with overfitting issues.

  • True
  • False

πŸ’‘ Hint: Consider what happens when a model struggles with unseen data.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset of 1000 samples, describe how you would perform 10-fold cross-validation. What insights might you gain?

πŸ’‘ Hint: Think about how each fold contributes to a clearer picture of model performance.

Question 2

Discuss how the choice of k in k-fold cross-validation influences the evaluation process, considering computational time and bias.

πŸ’‘ Hint: Reflect on the trade-offs involved in choosing k.

Challenge and get performance evaluation