When should I use Leave-One-Out Cross-Validation (LOOCV)?

What is Leave-One-Out Cross-Validation (LOOCV)?

LOOCV is a model validation technique where one observation is used for testing and the rest for training.

When should I use LOOCV instead of other cross-validation methods?

LOOCV is ideal for small datasets where you want to maximize training data, but it can be computationally expensive.

Are there any downsides to using LOOCV?

Yes, LOOCV can lead to high variance in the model's performance estimate and is computationally intensive.

What are some alternatives to LOOCV?

Alternatives include k-fold cross-validation, stratified k-fold, and train-test splits, which are less computationally demanding.