Browsing the model

  1. Open the model nugget. The Model tab initially shows the estimated the accuracy of the predictions for each offer and the relative importance of each predictor in estimating the model.

    To display the correlation of each predictor with the target variable, choose Association with Response from the View list in the right-hand pane.

  2. To switch between each of the four offers for which there are predictions, select the required offer from the View list in the left-hand pane.
    Figure 1. SLRM model nugget
    SLRM model nugget
  3. Close the model nugget window.
  4. On the stream canvas, disconnect the IBM® SPSS® Statistics File source node pointing to pm_customer_train1.sav.
  5. Add a Statistics File source node pointing to pm_customer_train2.sav, located in the Demos folder of your IBM SPSS Modeler installation, and connect it to the Filler node.
    Figure 2. Attaching second data source to SLRM stream
    Attaching second data source to SLRM stream
  6. On the Model tab of the SLRM node, select Continue training existing model.
    Figure 3. Continue training model
    Continue training model
  7. Click Run to re-create the model nugget. To view its details, double-click the nugget on the canvas.

    The Model tab now shows the revised estimates of the accuracy of the predictions for each offer.

  8. Add a Statistics File source node pointing to pm_customer_train3.sav, located in the Demos folder of your IBM SPSS Modeler installation, and connect it to the Filler node.
    Figure 4. Attaching third data source to SLRM stream
    Attaching third data source to SLRM stream
  9. Click Run to re-create the model nugget once more. To view its details, double-click the nugget on the canvas.
  10. The Model tab now shows the final estimated accuracy of the predictions for each offer.

    As you can see, the average accuracy fell slightly (from 86.9% to 85.4%) as you added the additional data sources; however, this fluctuation is a minimal amount and may be attributed to slight anomalies within the available data.

    Figure 5. Updated SLRM model nugget
    Updated SLRM model nugget
  11. Attach a Table node to the last (third) generated model and execute the Table node.
  12. Scroll across to the right of the table. The predictions show which offers a customer is most likely to accept and the confidence that they will accept, depending on each customer's details.

For example, in the first line of the table shown, there is only a 13.2% confidence rating (denoted by the value 0.132 in the $SC-campaign-1 column) ) that a customer who previously took out a car loan will accept a pension if offered one . However, the second and third lines show two more customers who also took out a car loan; in their cases, there is a 95.7% confidence that they, and other customers with similar histories, would open a savings account if offered one, and over 80% confidence that they would accept a pension.

Figure 6. Model output - predicted offers and confidences
Model output - predicted offers and confidences

Explanations of the mathematical foundations of the modeling methods used in IBM SPSS Modeler are listed in the IBM SPSS Modeler Algorithms Guide, available as a PDF file as part of your product download.

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 would use a Partition node to hold out a subset of records for purposes of testing and validation.