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.
Incorrect Answers:
B: BanditPolicy defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
The Bandit policy takes the following configuration parameters:
slack_factor: The amount of slack allowed with respect to the best performing training run. This factor specifies the slack as a ratio.
D: The Truncation selection policy periodically cancels the given percentage of runs that rank the lowest for their performance on the primary metric. The policy strives for fairness in ranking the runs by accounting for improving model performance with training time. When ranking a relatively young run, the policy uses the corresponding (and earlier) performance of older runs for comparison. Therefore, runs aren't terminated for having a lower performance because they have run for less time than other runs.
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