AI engineering glossary · ML fundamentals
What is SRM (structural risk minimisation)?
Structural risk minimisation (also called SRM): A principle that chooses a model by balancing how well it fits the training data against how complex its model class is, rather than only minimising training error. It underlies regularisation and margin-based methods and links to theoretical bounds on generalisation.
Structural risk minimisation: an example
Choosing the degree of a polynomial by minimising training error plus a penalty that grows with degree.
Related terms
More ML fundamentals terms
- Supervised learning
- Target leakage
- Temporal leakage
- Test set
- Time-series split
- Train-test contamination
- Training
- Training error
Where you learn it
Structural 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.