AI engineering glossary · ML fundamentals
What is hypothesis space?
Hypothesis space (also called Hypothesis class): The full set of candidate functions a learning algorithm is able to choose from. A linear model can only pick straight-line relationships, while a deep network can pick vastly more complex ones. Its size affects both how well data can be fit and how much data is needed to generalise.
Hypothesis space: an example
Straight lines y = mx + c form the hypothesis space for simple linear regression, so a curved relationship cannot be represented exactly.
Related terms
More ML fundamentals terms
- Independent and identically distributed
- Inductive bias
- Inference
- Inter-annotator agreement
- Irreducible error
- k-fold cross-validation
- L1 regularisation
- L2 regularisation
Where you learn it
Hypothesis space is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.