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Module

Monitoring adaptive models

3 Topics

40 mins

Visible to all users
Beginner Pega Customer Decision Hub 8.7 Pega Customer Decision Hub 8.6 English

It is a regular data scientist task to inspect the health of the adaptive models and share the findings with the business. The predictive performance and success rate of individual adaptive models provide information that can help business users and decisioning consultants to refine business processes. The content of this module showcases adaptive models used in Customer Decision Hub predictions that aim to optimize customer engagement but is equally relevant for case management predictions.

Learn how to monitor the performance of the adaptive models and how to export the raw data that adaptive models have processed to inspect and validate the predictors.

After completing this module, you should be able to:

Name the key metrics of adaptive models visualized in the bubble chart
Inspect individual active and inactive predictors
Explain how predictors with similar predictive performance are grouped
Examine the propensity distribution and the trend for the whole model
Export the raw data that is used by adaptive models

Practice what you learned in the following Challenges:

Monitoring adaptive models Exporting historical data

Available in the following missions:

Pega Process AI Essentials AI for 1:1 Customer Engagement

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