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.
Table of Contents
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.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.
