When should I use leave-one-out validation?

When should I use leave-one-out validation?

Leave-one-out validation (LOOV) is useful when you have a small dataset and want to maximize training data.

What are the advantages of using LOOV over k-fold validation?

LOOV provides a more thorough evaluation since it uses almost all data for training, but it's computationally expensive.

Are there any downsides to using LOOV?

Yes, LOOV can lead to high variance in performance estimates, especially with noisy data.

In what scenarios is LOOV not recommended?

It's not recommended for large datasets due to its computational cost and for datasets with high variability.

Can you give an example of when LOOV would be beneficial?

LOOV is beneficial in medical studies with limited patient data, allowing for robust model evaluation.