Building the flow

Figure 1. Example flow to classify customers using multinomial logistic regression
Example flow to classify customers using multinomial logistic regression
  1. Add a Data Asset node that points to telco.csv.
  2. Add a Type node, double-click it to open its properties, and click Read Values. Make sure all measurement levels are set correctly. For example, most fields with values of 0.0 and 1.0 can be regarded as flags.
    Figure 2. Measurement levels
    Measurement levels

    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.

  3. Set the role for the custcat field to Target. Leave the role for all other fields set to Input.
  4. 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 will be excluded for the purpose of this analysis. To filter them out, in the Filter node properties, click Add Columns and select the fields to exclude.
    Figure 3. Filtering on demographic fields
    Filtering on demographic fields

    (Alternatively, you could change the role to None for these fields rather than excluding them, or select the fields you want to use in the modeling node.)

  5. In the Logistic node properties, under MODEL SETTINGS, select the Stepwise method. Also select Multinomial, Main Effects, and Include constant in equation.
    Figure 4. Example flow to classify customers using multinomial logistic regression
    Example flow to classify customers using multinomial logistic regression
  6. Under EXPERT OPTIONS, select Expert mode, expand the Output section, and select Classification table.
    Figure 5. Example flow to classify customers using multinomial logistic regression
    Example flow to classify customers using multinomial logistic regression