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Monitoring adaptive models

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It is a regular data scientist task to inspect the health of the out-of-the-box Pega Customer Decision Hub™ predictions and the adaptive models that drive them, and share the findings with the business team. The predictive performance and success rate of individual adaptive models provide information that can help business users and decisioning architects to refine business processes. Learn how to monitor the performance of predictions, adaptive models, and predictors.

After completing this module, you should be able to:

Describe the lift metrics of a prediction
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

Practice what you learned in the following Challenge:

Monitoring adaptive models v6

Available in the following mission:

AI for 1:1 Customer Engagement v3

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