AI engineering glossary · ML fundamentals
What is label noise?
Label noise (also called Noisy labels): Errors or inconsistency in the labels of a training set, caused by human mistakes, ambiguous cases or faulty automatic tagging. It can mislead learning and cap achievable accuracy, so cleaning labels often helps more than changing the model.
Label noise: an example
About 5% of images in a cat-versus-dog dataset are tagged with the wrong animal.
Related terms
More ML fundamentals terms
- Latent variable
- Learning curve
- Leave-one-out cross-validation
- Linear separability
- Machine learning
- Majority-class baseline
- Manifold hypothesis
- Meta-learning
Where you learn it
Label noise is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.