Browsing the Model

  1. Execute the node to generate the model, which is added to the Models palette in the upper-right corner. To view its details, right-click on the generated model node and choose Browse.

The model tab displays the equations used to assign records to each category of the target field. There are four possible categories, one of which is the base category for which no equation details are shown. Details are shown for the remaining three equations, where category 3 represents Plus Service, and so on.

Figure 1. Browsing the model results
Browsing the model results

The Summary tab shows (among other things) the target and inputs (predictor fields) used by the model. Note that these are the fields that were actually chosen based on the Stepwise method, not the complete list submitted for consideration.

Figure 2. Model summary showing target and input fields
Model summary showing target and input fields

The items shown on the Advanced tab depend on the options selected on the Advanced Output dialog box in the modeling node.

One item that is always shown is the Case Processing Summary, which shows the percentage of records that falls into each category of the target field. This gives you a null model to use as a basis for comparison.

Without building a model that used predictors, your best guess would be to assign all customers to the most common group, which is the one for Plus service.

Figure 3. Case processing summary
Case processing summary

Based on the training data, if you assigned all customers to the null model, you would be correct 281/1000 = 28.1% of the time. The Advanced tab contains further information that enables you to examine the model's predictions. You can then compare the predictions with the null model's results to see how well the model works with your data.

At the bottom of the Advanced tab, the Classification table shows the results for your model, which is correct 39.9% of the time.

In particular, your model excels at identifying Total Service customers (category 4) but does a very poor job of identifying E-service customers (category 2). If you want better accuracy for customers in category 2, you may need to find another predictor to identify them.

Figure 4. Classification table
Classification table

Depending on what you want to predict, the model may be perfectly adequate for your needs. For example, if you are not concerned with identifying customers in category 2, the model may be accurate enough for you. This may be the case where the E-service is a loss-leader that brings in little profit.

If, for example, your highest return on investment comes from customers who fall into category 3 or 4, the model may give you the information you need.

To assess how well the model actually fits the data, a number of diagnostics are available in the Advanced Output dialog box when you are building the model. Explanations of the mathematical foundations of the modeling methods used in IBM® SPSS® Modeler are listed in the IBM SPSS Modeler Algorithms Guide, available from the \Documentation directory of the installation disk.

Note also that these results are based on the training data only. To assess how well the model generalizes to other data in the real world, you can use a Partition node to hold out a subset of records for purposes of testing and validation.