AI engineering glossary · ML fundamentals
What is bias error?
Bias error (also called Bias): The part of a model's prediction error caused by wrong or overly simple assumptions, so that even with unlimited data it would miss the true relationship. It is the average gap between what the model predicts and the truth. Not to be confused with unfairness towards groups of people.
Bias error: an example
A straight-line model of house prices in a market where prices rise steeply with size will consistently underestimate large homes, whatever data it sees.
Related terms
More ML fundamentals terms
- Bias-variance tradeoff
- Classification
- Concept drift
- Cost-sensitive learning
- Covariate shift
- Cross-validation
- Curse of dimensionality
- Data leakage
Where you learn it
Bias 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.