AI engineering glossary · ML fundamentals
What is selection bias?
Selection bias (also called Sampling bias): A distortion that arises when the examples in a dataset are not representative of the population the model will serve, because of how they were collected or chosen. Models trained on such data can fail on the groups that were left out.
Selection bias: an example
A survey taken only online overrepresents younger, urban respondents and misses many rural households.
Related terms
More ML fundamentals terms
- Self-supervised learning
- Semi-supervised learning
- Shortcut learning
- Spurious correlation
- Stratified k-fold
- Structural risk minimisation
- Supervised learning
- Target leakage
Where you learn it
Selection bias is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.