You work as a machine learning specialist for a scientific instruments company. Your machine learning team has been assigned the task of developing a machine learning model that optimizes the electronic components in the production line to the current product development lifecycle. You have built your model using the XGBoost SageMaker built-in algorithm. You are now in the process of tuning the model hyperparameters. In your hyperparameter job, you have chosen the num_class, num_round, alpha, booster, early_stopping_rounds, min_child_weight, subsample, eta, and num_round hyperparameters to use in your optimization. When running your hyperparameter tuning job, you have noticed that the computational complexity of a hyperparameter tuning job is high. You are getting suboptimal results. Which option should you implement to get better results from your hyperparameter tuning job?

Answer options:

A.Reduce the number of hyperparameters in your optimization by removing the alpha hyperparameter from your hyperparameter tuning job.
B.Reduce the number of hyperparameters in your optimization by removing the eta hyperparameter from your hyperparameter tuning job.
C.Adjust the range of values for each of the hyperparameters to a smaller range of values.
D.Adjust the range of values for each of the hyperparameters to a larger range of values.

Correct Answer: C
Option A is incorrect. The alpha hyperparameter is one of the hyperparameters that have the greatest effect on optimizing the XGBoost evaluation metrics. Therefore, removing it will not help optimize your hyperparameter tuning job.
Option B is incorrect. The eta hyperparameter is one of the hyperparameters that have the greatest effect on optimizing the XGBoost evaluation metrics. Therefore, removing it will not help optimize your hyperparameter tuning job.
Option C is correct. Adjusting (limiting) the range of values that you search for each of the hyperparameters to a smaller range of values can significantly improve the success of hyperparameter optimization.
Option D is incorrect. Adjusting (expanding) the range of values that you search for each of the hyperparameters to a larger range of values will most likely make your hyperparameter optimization less optimal.
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 developer guide titled Best Practices for Hyperparameter Tuning (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html),
The Amazon SageMaker developer guide titled XGBoost Hyperparameters (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html),
The Amazon SageMaker developer guide titled Tune an XGBoost Model (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html),
The Amazon SageMaker developer guide titled Example: Hyperparameter Tuning Job (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex.html)