AI engineering glossary · ML fundamentals
What is model capacity?
Model capacity (also called Model complexity): A measure of how complicated a pattern a model is able to fit. Low-capacity models such as a straight line can only represent simple relationships; high-capacity ones such as large networks can fit almost anything, including noise. Matching capacity to the amount and complexity of data is central to avoiding both extremes of fit.
Model capacity: an example
A polynomial of degree 1 has low capacity; degree 15 fitted to 20 points has high capacity and can pass through every point.
Related terms
More ML fundamentals terms
- Model selection
- Model variance
- Nested cross-validation
- No free lunch theorem
- Noise
- Non-parametric model
- Occam's razor
- Online learning
Where you learn it
Model capacity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.