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?
Click on the arrows to vote for the correct answer
A. B. C. D.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.
The most suitable hyperparameter tuning technique for this scenario is Random Search (option D).
Hyperparameters are adjustable parameters that can significantly affect the performance of a machine learning algorithm. Fine-tuning these parameters can be time-consuming and requires running multiple experiments. Hyperparameter tuning techniques aim to automate this process and find the optimal combination of hyperparameters that result in the best model performance.
Random Search is a hyperparameter tuning technique that selects hyperparameters randomly within specified ranges for each hyperparameter. It performs multiple iterations, where each iteration involves training a model using a specific combination of hyperparameters. The performance of each model is evaluated using a validation set, and the best-performing model is selected.
Random Search is an effective technique for quickly finding the optimal hyperparameters. It has been shown to be more efficient than other techniques such as Grid Search, especially when the number of hyperparameters is large.
Bayesian Search (option A) is another hyperparameter tuning technique that uses Bayesian optimization to guide the search for optimal hyperparameters. It can be more efficient than Random Search when the number of hyperparameters is small. However, Bayesian Search requires prior knowledge about the probability distribution of the hyperparameters, which may not be available in all cases.
Hidden Markov Models (option B) and Conditional Random Fields (option C) are not hyperparameter tuning techniques. They are machine learning models used in natural language processing and other applications.
In conclusion, Random Search is the most suitable hyperparameter tuning technique for this scenario because it can quickly find the optimal hyperparameters for a binary classification algorithm with a large number of hyperparameters.