AI engineering glossary · ML fundamentals
What is generalisation error?
Generalisation error (also called Test error): The expected error of a model on new data drawn from the same source as its training data. It is approximated by error on a held-out test set, and it is what training is really trying to make small.
Generalisation error: an example
A model with 4% training error and 11% error on a fresh test set has an estimated value of about 11%.
Related terms
More ML fundamentals terms
- Generalisation gap
- Generative model
- Grid search
- Ground truth
- Group k-fold
- Holdout method
- Hyperparameter
- Hyperparameter tuning
Where you learn it
Generalisation 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.