The dataset is divided into two distinct subsets: a training set and a test set. The test set can be chosen randomly or based on the user's specific requirements. The model is trained on the training set, while the test set remains separate and is employed to evaluate the model's performance. Unlike cross-validation, the training set is not further divided into multiple folds for validation. Although this approach offers simplicity and computational efficiency, it may yield fluctuating performance estimates due to the inherent randomness in the split, especially with small or imbalanced datasets. To mitigate the impact of randomness and obtain more reliable performance estimates, we recommend repeating it multiple times and calculating average metrics from all splits.