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Monitoring adaptive models

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1 hr

Visible to: All users
Beginner Pega Customer Decision Hub 8.7 Pega Customer Decision Hub 8.6 English
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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.

Available in the following mission:

AI for 1:1 Customer Engagement v1

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