What are the advantages of Leave-One-Out Cross-Validation (LOOCV)?

What are the advantages of Leave-One-Out Cross-Validation (LOOCV)?

LOOCV is beneficial because it uses almost all data for training, providing a reliable estimate of model performance.

Are there any downsides to using LOOCV?

Yes, LOOCV can be computationally expensive, especially with large datasets, as it requires training the model multiple times.

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

LOOCV uses one observation for validation and the rest for training, while k-fold splits the data into k subsets, which can be more efficient.

In what scenarios is LOOCV preferred?

LOOCV is preferred when the dataset is small, as it maximizes the training data available for each model iteration.

Can LOOCV lead to overfitting?

LOOCV itself doesn't cause overfitting, but if the model is too complex, it may still overfit to the training data.