Vapnik-chervonenkis dimension (VC dimension)

The Vapnik-Chervonenkis dimension, or VC dimension, is a measure of the capacity of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter. It helps in understanding how well a model can be expected to perform on unseen data, based on its ability to fit a variety of patterns.