Practice Leave-One-Out Cross-Validation (LOOCV) - 12.3.D | 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 does LOOCV stand for?

💡 Hint: Think about what each point in the dataset represents in this technique.

Question 2

Easy

How many iterations does LOOCV perform on a dataset of 5 points?

💡 Hint: Each point gets a turn as the test set.

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 does LOOCV stand for?

  • Leave-One-Out Cross-Validation
  • Last-One-Out Cross-Validation
  • Leave-Some-Out Cross-Validation

💡 Hint: Think about what it means to leave one out!

Question 2

LOOCV has high computational costs compared to k-fold cross-validation.

  • True
  • False

💡 Hint: Consider how many times the model is trained compared to k-fold.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A company has collected 30 samples of customer data. If they wish to use LOOCV to evaluate their predictive model, calculate how many times the model needs to be trained.

💡 Hint: Remember, LOOCV trains on all but one sample each time.

Question 2

Discuss the implications of using LOOCV for a dataset with 10,000 samples in terms of time and computational resources.

💡 Hint: Think about resource allocation and the cost-effectiveness of model validation.

Challenge and get performance evaluation