You work as a machine learning specialist for a social media software company that produces games for mobile devices. Your company has a new game that they believe will generate a large following very quickly. You need to build a model to predict whether users will purchase additional game features via in-app purchases. You have a large dataset to use for your training, and you need to find the best hyperparameters by using a hyperparameter tuning job. You have configured the training jobs the hyperparameter tuning job will run by defining an estimator and objective. You want to run your training jobs in a highly parallel manner because you want to complete your hyperparameter tuning quickly. Also, you know that the order of magnitude is more important than the absolute value for your hyperparameter values. For example, a change from 1 to 2 is expected to have a much bigger impact than a change from 100 to 101. Which scaling type and search type combination should you use for your hyperparameter tuning job?

Answer options:

A.Logarithmic scaling and Bayesian search
B.Logarithmic scaling and Random search
C.Linear scaling and Bayesian search
D.Linear scaling and Random search

Correct Answer: B
Option A is incorrect. When an order of magnitude is more important than the absolute value for your hyperparameters in your tuning, you should use logarithmic scaling. However, when you wish to run many training jobs in parallel, you should use a Random search strategy, not a Bayesian search strategy.
Option B is correct. When an order of magnitude is more important than the absolute value for your hyperparameters in your tuning, you should use logarithmic scaling. When you wish to run many training jobs in parallel, you should use a Random search strategy.
Option C is incorrect. When an order of magnitude is more important than the absolute value for your hyperparameters in your tuning, you should use logarithmic scaling, not linear scaling. When you wish to run many training jobs in parallel, you should use a Random search strategy, not a Bayesian search strategy.
Option D is incorrect. When an order of magnitude is more important than the absolute value for your hyperparameters in your tuning, you should use logarithmic scaling, not linear scaling.
References:
Please see the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html),
The Amazon SageMaker Examples titled Random search and hyperparameter scaling with SageMaker XGBoost and Automatic Model Tuning (https://github.com/aws/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/xgboost_random_log/hpo_xgboost_random_log.ipynb)