AI-900: How to use metric score threshold to end Azure AI experiments

Learn how to configure the metric score threshold setting in Azure AI experiments to stop the training process when the model achieves a certain score or less on a primary metric.

Question

What setting should you configure if you want to end the experiment if the model achieves a certain score or less on normalized root mean squared error metric?

A. Blocked algorithms
B. Training compute target
C. Metric score threshold

Answer

C. Metric score threshold

Explanation

This metric causes the experiment to end if a model achieves a certain score (or less) on normalized root mean squared error.

The correct answer is C. Metric score threshold. This setting allows you to specify the minimum or maximum value of a primary metric that triggers the early termination of a run. For example, if you set the metric score threshold to 0.5 and the primary metric is normalized root mean squared error (NRMSE), the experiment will end if the NRMSE score is less than or equal to 0.5. This can help you save time and resources by stopping the training process when the model reaches a desired level of performance.

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