Scoring records
Earlier, we scored the same records used to estimate the model in order to evaluate how accurate the model was. Now we're going to see how to score a different set of records from the ones used to create the model. This is the goal of modeling with a target field: Study records for which you know the outcome, to identify patterns that will allow you to predict outcomes you don't yet know.
You could update the Statistics File source node to point to a different data file, or you could add a new source node that reads in the data you want to score. Either way, the new dataset must contain the same input fields used by the model (Age, Income level, Education and so on) but not the target field Credit rating.
Alternatively, you could add the model nugget to any stream that includes the expected input fields. Whether read from a file or a database, the source type doesn't matter as long as the field names and types match those used by the model.
You could also save the model nugget as a separate file, or export the model in PMML format for use with other applications that support this format, or store the model in an IBM® SPSS® Collaboration and Deployment Services repository, which offers enterprise-wide deployment, scoring, and management of models.
Regardless of the infrastructure used, the model itself works in the same way.