The Vapnik–Chervonenkis (VC) dimension is a fundamental concept in statistical learning theory and is used to measure the capacity or expressiveness of a class of functions (or models). Specifically, it quantifies how well a set of functions can fit or "shatter" a set of points in a given space.
