AI engineering glossary · ML fundamentals
What is k-fold cross-validation?
k-fold cross-validation (also called K-fold CV): The most common form of cross-validation. The data is divided into k equal folds; each fold takes one turn as the test portion while the other k-1 folds train the model. The k scores are averaged, and their spread hints at how stable the estimate is. Typical values of k are 5 or 10.
k-fold cross-validation: an example
With k = 5 and 1,000 rows, each round trains on 800 rows and tests on 200, giving five scores.
Related terms
More ML fundamentals terms
- L1 regularisation
- L2 regularisation
- Label
- Label noise
- Latent variable
- Learning curve
- Leave-one-out cross-validation
- Linear separability
Where you learn it
k-fold cross-validation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.