Exam DP-100 topic 3 question 119 discussion

Actual exam question from Microsoft's DP-100

Question #: 119
Topic #: 3

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You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.

The policy uses a slack_factor set to 0.1. an evaluation interval set to 1, and an evaluation delay set to 5.

You need to evaluate the outcome of the early termination policy.

What should you evaluate? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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Suggested Answer:

by rahuljain788 at March 23, 2023, 11:24 p.m.

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3a0b61c

8 months, 3 weeks ago

correct https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2#bandit-policy

upvoted 2 times

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rahuljain788

1 year, 8 months ago

https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html

upvoted 2 times

rahuljain788

1 year, 8 months ago

# In this example, the early termination policy is applied at every interval # when metrics are reported, starting at evaluation interval 5. Any run whose # best metric is less than (1 / (1 + 0.1)) or 91\% of the best performing run will # be terminated if (FALSE) { early_termination_policy = bandit_policy(slack_factor = 0.1, evaluation_interval = 1L, delay_evaluation = 5L) }

upvoted 4 times

snegnik

1 year, 6 months ago

evaluation delay set to 5 not evaluation interval

upvoted 2 times

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