What is model capacity in supervised learning?

Model capacity is the ability of a model to fit a wide range of functions.

Capacity refers to the complexity a model can represent. Low-capacity models (linear regression) can only capture simple relationships, while high-capacity models (deep neural networks) can represent highly complex functions. If capacity is too low, underfitting occurs; if too high, overfitting becomes likely without regularization.