The Vapnik-Chervonenkis (VC) dimension is a measure of the capacity or complexity of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter.
Named after Vladimir Vapnik and Alexey Chervonenkis.
A rigorous way to quantify model complexity in statistical learning theory.