V-Fold Cross-Validation

In v-fold cross-validation, repeated (v) random samples are drawn from the data for the analysis, and the respective model or prediction method, etc., is then applied to compute predicted values, classifications, etc. Typically, summary indices of the accuracy of the prediction are computed over the v replications; thus, this technique makes it possible for the analyst to evaluate the overall accuracy of the respective prediction model or method in repeatedly drawn random samples.

This method is customarily used in tree classification and regression methods (e.g., see  General Classification and Regression Tree Models (GC&RT), General CHAID Models (GCHAID), Classification Trees), or Interactive Trees (GC&RT, CHAID).