AI engineering glossary · ML fundamentals
What is LOOCV (leave-one-out cross-validation)?
Leave-one-out cross-validation (also called LOOCV): An extreme form of cross-validation where each single example takes a turn as the test set and the model trains on all the rest. It uses nearly all data each time and has little bias, but requires as many training runs as there are examples, so is only practical for small datasets or cheap models.
Leave-one-out cross-validation: an example
With 50 patient records, the model is trained 50 times, each time predicting the one patient left out.
Related terms
More ML fundamentals terms
- Linear separability
- Machine learning
- Majority-class baseline
- Manifold hypothesis
- Meta-learning
- Model
- Model capacity
- Model selection
Where you learn it
Leave-one-out 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.