In words, the sample complexity <math>n(\rho,\epsilon,\delta)</math> defines the rate of consistency of the algorithm.: Givengiven a desired accuracy <math>\epsilon</math> and confidence <math>\delta</math>, one needs to sample at most <math>n(\rho,\epsilon,\delta)</math> data points to guarantee that the risk of the output function is within <math>\epsilon</math> of the best possible, with probability at least <math>1-\delta</math>.<ref name = "Rosasco">{{citation |last = Rosasco | first = Lorenzo | title = Consistency, Learnability, and Regularization | series = Lecture Notes for MIT Course 9.520. | year = 2014 }}</ref>
