AI engineering glossary · ML fundamentals
What is irreducible error?
Irreducible error (also called Bayes error, Noise floor): The portion of prediction error that no model can remove, because the outcome depends on random factors or unmeasured influences. It sets a floor on achievable performance. Knowing it exists prevents wasted effort chasing perfection on inherently noisy targets.
Irreducible error: an example
Two identical-looking customers may differ in whether they buy simply because of mood or timing that the data never records.
Related terms
More ML fundamentals terms
- k-fold cross-validation
- L1 regularisation
- L2 regularisation
- Label
- Label noise
- Latent variable
- Learning curve
- Leave-one-out cross-validation
Where you learn it
Irreducible error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.