AI engineering glossary · ML fundamentals
What is ERM (empirical risk minimisation)?
Empirical risk minimisation (also called ERM): The principle of choosing the model whose average loss on the training data is smallest, as a stand-in for its unknowable average loss on all future data. Most supervised training follows it. It works well only when the model class is not so flexible that it simply memorises the sample.
Empirical risk minimisation: an example
Fitting a line by choosing slope and intercept that minimise the mean squared error over 1,000 training houses applies this principle.
Related terms
More ML fundamentals terms
- Feature
- Feature space
- Generalisation
- Generalisation error
- Generalisation gap
- Generative model
- Grid search
- Ground truth
Where you learn it
Empirical risk minimisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.