Developing and managing modelsPredictive analytics

Parent topic: Predictive analytics

Creating a predictive model

Use customer data to develop powerful and reliable models that can predict customer behavior, such as offer acceptance, churn rate, credit risk, or other types of behavior.

Creating a Pega predictive model
Use customer data to develop powerful and reliable models that can predict customer behavior, such as offer acceptance, churn rate, credit risk, or other types of behavior.
Importing a predictive model
Import predictive models from third-party tools to predict customer actions. You can import PMML and H2O models.
Connecting to an external model
You can run your custom artificial intelligence (AI) and machine learning (ML) models externally in third-party machine learning services. This way, you can implement custom predictive models in your decision strategies by connecting to models in the Google AI Platform and Amazon SageMaker machine learning services.
Configuring a predictive model
After you create a predictive model, configure the model outcome and source data settings to ensure that the predictions are accurate.
Developing models
The Model development step helps you create models for further analysis. You group predictors based on their behavior and create models to compare their key characteristics.
Analyzing models
In the Model analysis step you can compare and view scores of one or more predictive models in a graphical representation, analyze predictive models' score distribution, and compare the classification of scores of one or more predictive models.

Related information:

Predictive analytics

Parent topic: Predictive analytics