- In the navigation pane of Prediction Studio, click Models.
- In the header of the Models work area, click .
- In the Create adaptive model dialog box, enter the model Name and select the Business issue.
section, enter the customer responses to
the behavior you want to predict:
- To select an available positive outcome for the model, place the cursor in the empty field and, press Down Arrow, and click the outcome you want to use.
- To define a new positive outcome for the model, enter the outcome that you want to use.
For example: Use Accept to indicate that a customer accepted an offer.
section, enter which customer responses
represent the alternative outcome you want to predict:
- To select an available negative outcome for the model, place the cursor in the empty field, press the Down Arrow key, and click the outcome you want to use.
- To define a new negative outcome for the model, enter the outcome you want to use.
For example: Use Reject to indicate that a customer refused an offer.
- Optional: If online gradient boosting is enabled in your application, you can select the model
type in the Model type section by choosing from the following
- Different models per action - By default, different adaptive models are created for every action.
- Common model for all actions - This experimental option produces a single model for all actions by using the online gradient boosting machine learning technique. In the early preview version available in Pega Platform version 8.1, the model provides basic monitoring information.
- In the Context section, select the applicable class of the
model by performing the following actions:
- In the Apply to field, press Down Arrow, and select application class of the model.
- In the new fields that appear, select a development branch and a ruleset.
- Confirm the new adaptive model settings by clicking Create.
It is recommended that you add an extensive list of candidate predictors for your adaptive model instances to learn from. In the course of the learning process, adaptive models automatically select the best-performing predictors, which become active. The remaining predictors become inactive.
For more information, see About Adaptive Model rules.