AI engineering glossary · ML fundamentals
What is hyperparameter?
Hyperparameter: A setting chosen by the practitioner before training rather than learned from data. It controls how the model is built or how learning proceeds, for example tree depth, regularisation strength or number of neighbours. Good values are found by trying options and checking validation performance.
Hyperparameter: an example
Setting the maximum tree depth to 6 or the regularisation strength to 0.1 are hyperparameter choices tried on the validation set.
Related terms
More ML fundamentals terms
- Hyperparameter tuning
- Hypothesis space
- Independent and identically distributed
- Inductive bias
- Inference
- Inter-annotator agreement
- Irreducible error
- k-fold cross-validation
Where you learn it
Hyperparameter is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.