IBM SPSS Modeler Cookbook

Binning scale variables to address missing data

This recipe will tackle the issue of null values that are non-applicable rather than values that are unknown. When transactions are processed for modeling, invariably there will be certain transactions that are missing for a given case. In this recipe our cases will be customers. Imagine the straightforward instance that a customer, Bill Johnson, did not rent a horror movie within the last 12 months. The Using an @NULL multiple Derive to explore missing data recipe in Chapter 1, Data Understanding, helps determine if the presence or absence of such a value is predictive of the target. This recipe prepares the original variable for modeling. The issue addressed in this recipe is virtually guaranteed ...