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Hi, This is really more a stats question than a R one, but.... Does anyone have any familiarity with using the mean prediction squared error scaled by the variance of the response, as a 'scale free' criterion for evaluating different regression algorithms. E.g. Generate X_train, Y_train, X_test, Y_test from true f. X_test/Y_test are generated without noise, maybe? Use X_train, Y_train