What are the differences between K-Fold and Leave-One-Out cross-validation?

What are the differences between K-Fold and Leave-One-Out cross-validation?

K-Fold cross-validation divides the dataset into K subsets, while Leave-One-Out uses one data point as the test set.

Can you explain how K-Fold works in more detail?

Sure! In K-Fold, the model is trained K times, each time using a different subset as the test set and the rest for training.

What are the advantages of using Leave-One-Out?

Leave-One-Out can be beneficial for small datasets as it maximizes the training data used for each model training.

Are there any disadvantages to using K-Fold?

Yes, K-Fold can be computationally expensive, especially with large datasets, as it requires training the model K times.