Trainvaltest

Trainvaltest is a data partitioning scheme used in supervised machine learning that divides a dataset into three disjoint subsets: training, validation, and test. The training set is used to fit the model, the validation set to tune hyperparameters and compare models, and the test set to provide an unbiased assessment of final performance on unseen data. This triple-split approach helps separate model selection from final evaluation.

Common practice involves choosing split ratios such as 60/20/20, 70/15/15, or, for large datasets, 80/10/10. When data is time-dependent, temporal order should be preserved and random shuffling avoided to prevent leakage and unrealistic performance estimates. If class distributions are imbalanced, stratified sampling can maintain label proportions across all three subsets.

Implementation considerations include preventing data leakage, ensuring that preprocessing steps are learned only from the training data and consistently applied to the validation and test sets. Nested cross-validation is sometimes used to optimize hyperparameters (inner loop on training/validation) while reporting performance from an outer test loop, providing robust estimates at the cost of increased computation.

Relation to other schemes: train/validation/test is closely related to train/test splits, but the explicit validation set helps with hyperparameter tuning before final evaluation. In some workflows, a held-out test set may be used only once, after model selection, to avoid optimistic bias.

Applications and reporting: Trainvaltest is common in ML pipelines, competitions, and research to deliver repeatable model development and transparent performance metrics (such as accuracy, precision, recall, or F1). It is particularly important in iterative model development, hyperparameter optimization, and when communicating results to stakeholders.