A theory score for how complex a model can be.
VC Dimension is like testing a bendy lunchbox divider. It splits every weird pile of fries and peas. Powerful, but maybe too bendy.
It estimates how flexible a model is. It helps warn you about overfitting.
PAC
VC Dimension helps PAC set provable bounds on learning from data.
SLT
VC Dimension is a core measure of model complexity in SLT.
Bias-Variance Tradeoff
A higher VC Dimension means more flexibility and more variance risk.
Regularization
Regularization limits effective complexity and helps reduce overfitting.