What is the impact of Leave-One-Out Cross-Validation on model selection?

What is the impact of Leave-One-Out Cross-Validation on model selection?

Leave-One-Out Cross-Validation (LOOCV) is a technique where one data point is used for testing and the rest for training. This method can provide a more accurate estimate of model performance, especially with small datasets.

How does LOOCV compare to k-fold cross-validation?

LOOCV uses a single observation for validation, while k-fold splits the dataset into k subsets. LOOCV can be computationally expensive but may yield less biased estimates for small datasets.

Are there any drawbacks to using LOOCV?

Yes, LOOCV can be computationally intensive, especially with large datasets, and it may lead to high variance in performance estimates.

When should I use LOOCV instead of other methods?

LOOCV is best used when you have a small dataset and want to maximize the training data for each model evaluation, but consider the computational cost.