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Monitoring adaptive models

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Data scientists must regularly inspect the health of the out-of-the-box Pega Customer Decision Hub™ predictions and the adaptive models that drive them, and share their 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 Challenges:

Monitoring predictions v5 Monitoring adaptive models v4

Available in the following mission:

AI for 1:1 Customer Engagement v2

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