Settings (Propensity to purchase)

Model Validation

Model validation creates training and testing groups for diagnostic purposes. If you select the classification table in the Diagnostic Output section, the table will be divided into training (selected) and testing (unselected) sections for comparison purposes. Do not select model validation unless you also select the classification table. The scores are based on the model generated from the training sample, which will always contain fewer records than the total number of available records. For example, the default training sample size is 50%, and a model built on only half the available records may not be as reliable as a model built on all available records.

• Training sample partition size (%). Specify the percentage of records to assign to the training sample. The rest of the records with non-missing values for the response field are assigned to the testing sample. The value must be greater than 0 and less than 100.
• Set seed to replicate results. Since records are randomly assigned to the training and testing samples, each time you run the procedure you may get different results, unless you always specify the same starting random number seed value.

Diagnostic Output

Overall model quality. Displays a bar chart of overall model quality, expressed as a value between 0 and 1. A good model should have a value greater than 0.5.

Classification table. Displays a table that compares predicted positive and negative responses to actual positive and negative responses. The overall accuracy rate can provide some indication of how well the model works, but you may be more interested in the percentage of correct predicted positive responses.

• Minimum probability. Assigns records with a score value greater than the specified value to the predicted positive response category in the classification table. The scores generated by the procedure represent the probability that the contact will respond positively (for example, make a purchase). As a general rule, you should specify a value close to your minimum target response rate, expressed as a proportion. For example, if you are interested in a response rate of at least 5%, specify 0.05. The value must be greater than 0 and less than 1.

Name and Label for Recoded Response Field

This procedure automatically recodes the response field into a new field in which 1 represents positive responses and 0 represents negative responses, and the analysis is performed on the recoded field. You can override the default name and label and provide your own. Names must conform to IBM® SPSS® Statistics naming rules. See the topic Variable names for more information.

Save Scores

A new field containing propensity scores is automatically saved to the original dataset. Scores represent the probability of a positive response, expressed as a proportion.

• Field names must conform to IBM SPSS Statistics naming rules. See the topic Variable names for more information.
• The field name cannot duplicate a field name that already exists in the dataset. If you run this procedure more than once on the same dataset, you will need to specify a different name each time.