AI engineering glossary · ML fundamentals
What is model selection?
Model selection: The task of choosing among candidate models or settings, using validation results or criteria such as information measures. It should be judged on data not used to fit the candidates, and reported honestly with a final untouched test.
Model selection: an example
Comparing a linear model, a tree and a small neural network by 5-fold cross-validation, then picking the best.
Related terms
More ML fundamentals terms
- Model variance
- Nested cross-validation
- No free lunch theorem
- Noise
- Non-parametric model
- Occam's razor
- Online learning
- Out-of-distribution
Where you learn it
Model selection is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.