Predictive Model rule form
|
|
The segmentation provided by the predictive model needs to be assigned to actions applying to a given class or class range. Defining predictive models depends on the type of model:
PAD models are constructed to generate the largest possible number of classes (segments) that exhibit predicted behavior, steadily increasing as the class number increases. However, business strategies translate to the two or three alternatives typically associated with the probability of predicted behavior (high, medium and low). Remapping the classification defined in the predictive model to the smaller number of business strategies allows you to increase the quality of business. For example, if a lower propensity class is reassigned to the medium propensity class where fewer customers are presented with a product offer but a greater proportion responds, although the volume of business decreases, the quality increases.
Graphical representation is provided for predictive model results. Two graphs side by side shows the score distribution.
The graph on the left allows is based on the non-aggregated classification; this is also the graph displayed in the Statistics tab. As you aggregate classes, the graph on the right displays aggregated statistics according to the number of results.
Map the classes output by the model to decision results. To understand the effect of combining the different classes to create predictive based segmentation, examine the grouped statistics in the graph displayed on the right.
If the original PAD model does not contain aggregation/grouping statistics information, N/A is displayed.
The results of a PMML model consist of model output fields. In this tab, you select the output field that provides the classification result. Typically, this is the model's default outcome field.