- In the navigation pane of Prediction Studio, click Models.
- In the header of the Models work area, click .
- In the New predictive model dialog box, enter a Name for your model.
- In the Create model section, click Select external model.
- In the Machine learning service list, select the ML
service from which you want to run the model.Pega Platform currently supports Google AI Platform and Amazon SageMaker models.
- In the Model list, select the model that you want to
run.The list contains all the models that are part of the authentication profile that is mapped to the selected service.
- In the Upload model metadata section, upload the model
metadata file with input mapping and outcome categories for the model:
- Download the template for the model metadata file in JSON format by clicking Download template.
- On your device, open the template model metadata file that you
downloaded and define the mapping of input fields to Pega Platform.For information about the JSON file fields and the available values, see Metadata file specification for predictive models.
- Save the model metadata file.
- In Prediction Studio, click Choose file, and then double-click the model metadata file.
- In the Context section, specify where you want to save the
- In the Apply to field, press the Down arrow key, and then click the class in which you want to save the model.
- Define the class context by selecting the appropriate values in the Development branch, Add to ruleset, and Ruleset version lists.
- Verify the settings, and then click Next.
- In the Outcome definition section, define what you want
the model to predict.Enter a meaningful value, for example,
- In the Predicting list, select the model type:
- For binary outcome models, select Two categories,
and then specify the categories that you want to predict.
Binary outcome models are models for which the predicted outcome is one of two possible outcome categories, for example, Churn or Loyal.
- For categorical outcome models, select More than two
categories, and then specify the categories that you want to
Categorical outcome models are models for which the predicted outcome is one of more than two possible outcome categories, for example, Red, Green, or Blue.
- For continuous outcome models, select A continuous
value, and then enter the value range that you want to
Continuous outcome models are models for which the predicted outcome is a value between a minimum and maximum value, for example, between 1 and 99.
- For binary outcome models, select Two categories, and then specify the categories that you want to predict.
- In the Expected performance field, enter a value that
represents the expected predictive performance of the model:
For more information about performance measurement metrics, see Metrics for measuring predictive performance.
- For binary models, enter an expected area under the curve (AUC) value between 50 and 100.
- For categorical models, enter an expected F-score performance value between 0 and 100.
- For continuous models, enter an expected RMSE value between 0 and 100.
- Review the model settings, and then click Create.
- Click Save.