Cross Validation

Cross validation refers to a procedure in which an analysis is performed on one dataset and the parameters used in a second dataset. At the same time, an analysis is performed on a second dataset and then the parameters are used on the first dataset. A successful cross validation would occur when the overall estimation of the process has equal accuracy for both datasets. The argument is that the resulting equation or estimation process generated using one set of data will cross validate or continue to be accurate when applied to an entirely new set of data. Similarly, going from the second set of data to the first set retains the same level of accuracy. The procedure is useful when the desire is ...