How do Leave-One-Out Cross-Validation and K-Fold Cross-Validation compare?

What are the main differences between Leave-One-Out Cross-Validation and K-Fold Cross-Validation?

Leave-One-Out Cross-Validation (LOOCV) uses one observation for testing and the rest for training, while K-Fold splits the data into K subsets.

Which method is better for small datasets?

LOOCV is often better for small datasets as it maximizes the training data, but it can be computationally expensive.

What are the advantages of K-Fold over LOOCV?

K-Fold is generally faster and provides a better estimate of model performance by averaging results over multiple folds.

Can you give an example of when to use each method?

Use LOOCV for small datasets where every data point is crucial, and K-Fold for larger datasets to balance training and testing.