the prediction quality of an approximation model may be improved if unimportant variables are removed from the model. this idea is adopted in the metamodel of optimal prognosis (mop) which is based on the search for the optimal input variable set and the most appropriate approximation model (polynomial or moving least squares with linear or quadratic basis). due to the model independence and objectivity of the cop measure, it is well suited to compare the different models in the different subspaces.