After you have developed and evaluated a model based on historical data, you can apply the model to new data in order to make predictions. In predictive analytics, this is called scoring. You score cases for which the outcome is not yet known. Your evaluation of the historical data gives you a sense of how the model is likely to perform in the new situation.
One way to implement scoring is to make use of the classification function coefficients. Here is the syntax in which the classification function coefficients are used in compute:
compute cf1=57.351*alcohol+.854*malic_acid+39.031*ash
-.662*ash_alcalinity+.502*magnesium-3.261*total_phenols
+3.579*flavanoids+39.626*nonflavanoid_phenols+1.243*proanthocyanins
-3.988*color_intensity+27.600*hue+22.527*dilution
+.021*proline-523.443.
compute cf2=52.373*alcohol+.134*malic_acid+28.029*ash
+.465*ash_alcalinity...
