In LOOCV, the dataset is divided into n subsets, where n is the total number of data points. The model is trained n times, each time leaving out one unique data point for validation while using the remaining n-1 points for training. The process involves the following steps:
1. Data Splitting: Divide the dataset into n subsets. 2. Model Training: Train the model n times, each time leaving out one subset for validation. 3. Validation: Validate the model on the left-out subset. 4. Performance Aggregation: Aggregate the performance metrics from each iteration to get an overall estimate.
