trainvalidtest

Trainvalidtest refers to the common practice of partitioning a dataset into three disjoint subsets: a training set used to fit the model, a validation set used to tune hyperparameters and perform model selection, and a test set used to estimate final performance on unseen data.

Split sizes vary with data size and domain. Common schemes include 70/15/15, 60/20/20, or 80/10/10 for larger datasets. When data are scarce, cross-validation or nested cross-validation can provide more reliable estimates while still producing a held-out evaluation set.

Roles: training builds the model; validation informs hyperparameter choices, feature engineering decisions, and early stopping; test provides an unbiased assessment of generalization after the final model is chosen.

Practical considerations include stratified sampling to preserve class distributions for classification tasks, and ensuring splits are time-consistent for temporal data. Avoid data leakage by randomizing with a fixed seed and keeping the test set separate from any tuning process.

In practice, train/validation/test is often used alongside or within cross-validation workflows. The key idea is to separate learning, model selection, and final evaluation to obtain realistic performance estimates.