# Hold-out set

URL: https://sellmydatatoai.net/glossary/hold-out-set

A hold-out set is a portion of data kept out of training so it can test how well a model performs on examples it has never seen.

A hold-out set is a portion of data withheld from training and used only to test a model, so its results show how well the model generalizes to unseen examples. Data is commonly split into training, validation and test sets: the validation set guides tuning and the test set gives the final score.

## What is contamination?

Contamination is what happens when hold-out examples, or near copies of them, end up in training data. Scores then overstate real ability. It is a growing problem for large language models trained on web-scale text, because published benchmarks are copied across the internet.

## Why does a hold-out set matter in a data deal?

A hold-out set matters in a data deal in two ways. Buyers can hold back part of a delivered dataset to test whether it improves their models before final payment. Sellers of [evaluation datasets](https://sellmydatatoai.net/glossary/evaluation-dataset) need contract terms that keep the set out of training.

See [how to evaluate a dataset sample](https://sellmydatatoai.net/guides/how-to-evaluate-dataset-samples) and [representative sample](https://sellmydatatoai.net/glossary/representative-sample).