Leave-One-Out Cross-Validation (LOOCV)

Leave-One-Out Cross-Validation (LOOCV) is a specific type of cross-validation where each single data point in the dataset is used as the test set, while the remaining data points form the training set. This process is repeated for every data point, resulting in as many models being trained and evaluated as there are data points in the original dataset. The final performance metric is then calculated by averaging the performance across all these individual evaluations.

Visit the following resources to learn more:

• @article@LOOCV for Evaluating Machine Learning Algorithms