In statistics the mean squared prediction error (MSPE), also known as mean squared error of the predictions, of a smoothing, curve fitting, or regression procedure is the expected value of the squared prediction errors (PE), the square difference between the fitted values implied by the predictive function ${\displaystyle {\widehat {g}}}$ and the values of the (unobservable) true value g. It is an inverse measure of the explanatory power of ${\displaystyle {\widehat {g}},}$ and can be used in the process of cross-validation of an estimated model. Knowledge of g would be required in order to calculate the MSPE exactly; in practice, MSPE is estimated.