Holdoutset

Holdoutset, or holdout set, is a portion of a dataset that is withheld from the training process and reserved for evaluating a model's performance on unseen data. It provides an unbiased estimate of how the model will perform on new examples and helps guard against overfitting to the training data.

Formation and size: The holdoutset is typically formed by random sampling from the full dataset. In classification tasks, stratified sampling may be used to preserve class proportions. Common practice reserves about 10–30% of the data as the holdoutset, with the remaining data used for training. Some workflows separate a validation or development set for tuning and a distinct holdoutset for final evaluation.

Relationship to other splits: In many workflows, the holdoutset corresponds to the test set, while a separate validation set is used during training for hyperparameter selection. In other cases, cross-validation is used to estimate performance without a fixed holdout, while a final holdoutset provides final evaluation.

Usage and metrics: After training, performance metrics such as accuracy, precision, recall, ROC-AUC, mean squared error, or others are computed on the holdoutset. This evaluation informs model selection, hyperparameter tuning, and comparisons across models while aiming to reflect performance on future data.

Limitations: A holdoutset must be representative of the target population; otherwise the estimate may be biased. With limited data, withholding a holdout can reduce the training signal. If the holdout is reused across experiments or if distribution shifts occur between the holdout and future data, estimates may be overly optimistic or pessimistic.

See also: holdout method, train/test split, validation set, test set, cross-validation.