You work as a machine learning specialist for a healthcare insurance company. Your company wishes to determine which registered plan participants will choose a new health care option your company plans to release. The roll-out plan for the new option is compressed, so you need to produce results quickly. You plan to use a binary classification algorithm on this problem. In order to find the optimal model quickly, you plan to run the maximum number of concurrent hyperparameter training jobs to reach the best hyperparameter values. Which of the following types of hyperparameters tuning techniques will best suit your needs?

Answer options:

A.Bayesian Search
B.Hidden Markov Models
C.Conditional Random Fields
D.Random Search

Answer: D
Option A is incorrect. Bayesian Search uses regression to choose sets of hyperparameters to test iteratively. Due to this iterative approach, this method cannot run the maximum number of concurrent training jobs without impacting the performance of the search. Therefore, this method will take longer than the Random Search method.
Option B is incorrect. The Hidden Markov Model is a class of probabilistic graphical model. It is not used by SageMaker for hyperparameter tuning.
Option C is incorrect. Conditional Random Fields is a type of discriminative classifier. It is not used by SageMaker for hyperparameter tuning.
Option D is correct. The Random Search technique allows you to run the maximum number of concurrent training jobs without impacting the performance of the search. Therefore, getting you to your optimized hyperparameters quickly.
Reference:
Please see the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works.