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Artificial Intelligence & Machine Learning vocabulary · Evaluation and Metrics

Cross-validation

Meaning in artificial intelligence & machine learning, with examples

/ˌkrɔːs vælɪˈdeɪʃən/nounCommon term

In artificial intelligence & machine learning, cross-validation means: An evaluation method that splits the data into several parts, trains on all but one and tests on the remaining part, repeating until every part has been used for testing.

In Turkish: çapraz doğrulama

How it is used

The most common form is k-fold cross-validation, for example 5-fold. It gives a more reliable estimate than a single split, especially on small datasets, but needs k times as much training. It is less common with very large models.

Example sentences

  1. With only 800 patients, we used 10-fold cross-validation instead of a fixed test set.

  2. The scores vary a lot across the cross-validation folds.

Common collocations

  • k-fold cross-validation
  • 5-fold cross-validation
  • cross-validation score
  • cross-validation folds
  • validation setA part of the data kept out of training and used during development to compare models, tune settings and decide when to stop training.
  • test setA part of the data kept aside and used only at the end, to measure how well a finished model works on examples it has never seen.
  • overfittingA problem in which a model learns its training data too closely, including noise and accidents, so it scores well on that data but poorly on new data.
  • hyperparameterA setting chosen by engineers before training rather than learned from data, such as the learning rate, the batch size or the number of layers.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Evaluation and Metrics

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