The Vapnik–Chervonenkis theory (VC Theory) is a theoretical machine learning framework created by Vladimir Vapnik and Alexey Chervonenkis.It aims to quantify the capability of a learning algorithm and could be considered to be the main sub-field of statistical learning theory.One of the main elements of the VC theory is the Vapnik-chervonenkis dimension (VC dimension). It quantifies the complexity of hypothesis space. It comes up with an estimation of the capability or capacity of a classification machine learning algorithm for a particular dataset (number and dimensionality of examples)