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Question 16 - MLS-C01 discussion

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A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.

Which solution provides the MOST accurate result?

A.
Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this HPO job.
Answers
A.
Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this HPO job.
B.
Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job: [0.01, 0.1] [0.001, 0.01] [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs.
Answers
B.
Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job: [0.01, 0.1] [0.001, 0.01] [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs.
C.
Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this training job.
Answers
C.
Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this training job.
D.
Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three: [0.01, 0.1] [0.001, 0.01] [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs.
Answers
D.
Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three: [0.01, 0.1] [0.001, 0.01] [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs.
Suggested answer: C

Explanation:

The solution C modifies the HPO configuration to use a logarithmic scale for the learning rate parameter. This means that the values of the learning rate are sampled from a log-uniform distribution, which gives more weight to smaller values. This can help to explore the lower end of the range more evenly and find the optimal learning rate more efficiently. The other solutions either use a linear scale, which may not sample enough values from the lower end, or divide the range into sub-intervals, which may miss some combinations of hyperparameters.References:

How Hyperparameter Tuning Works - Amazon SageMaker

Tuning Hyperparameters - Amazon SageMaker

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asked 16/09/2024
Jonathan Hernández Hernández
31 questions
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