Building the Stream

Add a Statistics File source node pointing to telco.sav in the Demos folder.
Figure 1. Sample stream to classify customers using multinomial logistic regression
1. Add a Type node and click Read Values, making sure that all measurement levels are set correctly. For example, most fields with values 0 and 1 can be regarded as flags.
  Figure 2. Setting the measurement level for multiple fields
  
  Tip: To change properties for multiple fields with similar values (such as 0/1), click the Values column header to sort fields by value, and then hold down the shift key while using the mouse or arrow keys to select all the fields you want to change. You can then right-click on the selection to change the measurement level or other attributes of the selected fields.
  
  Notice that gender is more correctly considered as a field with a set of two values, instead of a flag, so leave its Measurement value as Nominal.
2. Set the role for the custcat field to Target. All other fields should have their role set to Input.
Figure 3. Setting field role

Since this example focuses on demographics, use a Filter node to include only the relevant fields (region, age, marital, address, income, ed, employ, retire, gender, reside, and custcat). Other fields can be excluded for the purpose of this analysis.

Figure 4. Filtering on demographic fields

(Alternatively, you could change the role to None for these fields rather than exclude them, or select the fields you want to use in the modeling node.)
In the Logistic node, click the Model tab and select the Stepwise method. Select Multinomial, Main Effects, and Include constant in equation as well.
Figure 5. Choosing model options

Leave the Base category for target as 1. The model will compare other customers to those who subscribe to the Basic Service.
On the Expert tab, select the Expert mode, select Output, and, in the Advanced Output dialog box, select Classification table.