Leave-One-Out Cross-Validation (LOOCV) is a specific type of K-fold cross-validation where the number of folds (K) equals the total number of data points (N) in your dataset.
Here is how it works in a nutshell:
The Process: If you have N data points, you train your model N times. In each iteration, you "leave out" exactly one data point to use as the test set and train the model on the remaining N-1 data points.
The Evaluation: You record the model's error on that single held-out point. After rotating through all data points, you average the N individual errors to get the final validation score.
