Cross-Validation Techniques Quiz

What you'll be asked

1. In the context of model evaluation, what is the primary purpose of cross-validation?
2. When using K-Fold cross-validation, what happens to the training process as the value of 'K' increases?
3. Which cross-validation technique is most appropriate for a classification problem with a highly imbalanced target class?
4. You are working with time-series data on Azure. Why should you avoid using standard K-Fold cross-validation?
5. In a Leave-One-Out Cross-Validation (LOOCV) scenario with a dataset of size N, which of the following statements is true regarding the model's variance?

Answers and explanations come after you submit.