HoldoutSplitting

Holdout splitting is a simple method used in machine learning to evaluate a model’s generalization by partitioning a dataset into separate subsets. Typically, the data are divided into a training set, used to fit the model, and a holdout test set, used to estimate performance on unseen data. In practice, a validation set may also be created to tune hyperparameters, with the final assessment reported on the holdout test set.

The split is usually performed randomly, and often stratified to preserve the distribution of target labels, especially for imbalanced datasets. Common split ratios include 70/30 or 80/20 for training versus test. Repeating the holdout process with different random seeds (repeated holdout) can provide a more stable performance estimate, albeit at additional computational cost. In time-series applications, holdout splitting is typically non-random and respects temporal order, training on past data and testing on future data.

Advantages of holdout splitting include its simplicity and low computational overhead, making it suitable for large datasets or quick evaluations. It provides a near-independent assessment of model performance when the test set remains unseen during training. Limitations include high variance in estimates when the dataset is small, and sensitivity to how the split is made. It can also lead to optimistic or pessimistic results if leakage occurs or if the split does not reflect the data distribution.

Best practices involve fixing a random seed for reproducibility, using stratified splits for class balance, and considering nested approaches when hyperparameter tuning is required, to prevent information from leaking from the test portion into model selection. Document the split ratio, seed, and methodology to ensure reproducibility.