Training and Validation Quiz

What you'll be asked

1. What is the primary purpose of splitting a dataset into training and validation sets?
2. If a model performs exceptionally well on the training set but poorly on the validation set, what is the most likely issue?
3. Which of the following describes the 'Hold-out' validation method?
4. Why is it considered a 'best practice' to shuffle your dataset before splitting it into training and validation sets?
5. In a scenario where you have a very small dataset, why might K-Fold Cross-Validation be preferred over a simple train-validation split?

Answers and explanations come after you submit.