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Monitoring adaptive models

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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 the next best actions of the company. Learn how to monitor the performance of the adaptive models and measure the lift that the system produces.

Learn 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:

Describe how predictions add best practices to predictive models
Explain how the use of a model control group allows the measurement of lift
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:

Data Scientist v3

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