AI engineering glossary · ML fundamentals
What is IID (independent and identically distributed)?
Independent and identically distributed (also called IID, i.i.d.): The standard assumption that every example is drawn separately from the same underlying source of data, and no example influences another. Most learning theory and validation methods rely on it, and real-world violations such as time trends or repeated users cause surprises.
Independent and identically distributed: an example
Customers drawn at random from one city's database roughly fit it; daily stock prices, which depend on the previous day, do not.
Related terms
More ML fundamentals terms
- Inductive bias
- Inference
- Inter-annotator agreement
- Irreducible error
- k-fold cross-validation
- L1 regularisation
- L2 regularisation
- Label
Where you learn it
Independent and identically distributed is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.