Which two early termination policies should you use?

You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance.

You need to select an early termination policy that meets the following requirements:

• accounts for the performance of all previous runs when evaluating the current run

• avoids comparing the current run with only the best performing run to date

Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A . Bandit
B . Median stopping
C . Default
D . Truncation selection

View Answer

Answer: B,C

Explanation:

The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.

If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.medianstoppingpolicy

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.truncationselectionpolicy

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy