In this video, we learn a hack to increase the size of our training set while still being able to do validation: cross validation.

Link to my notes on Introduction to Data Science: [ Ссылка ]

Try answering these comprehension questions to further grill in the concepts covered in this video:

1. When should you not use cross validation?
2. What is the optimal size for validation? What about cross validation?
3. Can we do cross validation on time series data?
4. Can we do cross validation on data with heavy class imbalance?
5. Does cross validation help better with high variance or high bias models?

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