In order to evaluate the appropriateness of the model, MARS uses generalized cross-validation (GCV) which is residual square errors penalized by a function related to complexity of the model [22]. The numerator in GCV is the average residual squared error and the denominator is a penalty term that reflects model complexity. The use of the denominator is to prohibit selection of a model with many terms that decreases only slightly the residual errors. The GCV statistic is an estimate of the variance for error in a regression model that includes a penalty term for the number of parameters used in the regression. The GCV R-squared statistic is the ordinary R-squared statistic calculated with the variance for error replaced with the GCV statistic [22].