You work as a machine learning specialist for a book publishing firm. Your firm is releasing a new publication and would like to use a machine learning model to structure a marketing campaign for the new publication to decide whether to market to each of their registered customers or not. You and your machine learning team have developed a model using the XGBoost SageMaker built-in algorithm. You are now at the hyperparameter optimization stage, where you are trying to find the best version of your model by running several training jobs on your data using your XGBoost algorithm. How do you configure your hyperparameter tuning jobs to get a recommendation for the best values for your hyperparameters?

Answer options:

A.Set the eta, alpha, and min_child_weight to specific values, and set the max_depth to a range of values. Choose to minimize the area under the curve (auc) as your optimization metric.
B.Set ranges of values for the eta, alpha, and min_child_weight, and max_depth hyperparameters. Choose to maximize the area under the curve (auc) as your optimization metric.
C.Set ranges of values for the eta, alpha, and min_child_weight, and max_depth hyperparameters. Choose to minimize the normalized discounted cumulative gain (ndcg) as your optimization metric.
D.Set ranges of values for the eta, alpha, and min_child_weight, and max_depth hyperparameters. Launch one training job. Choose to maximize the area under the curve (auc) as your optimization metric.

Correct Answer: B
Option A is incorrect. You do not want to restrict your hyperparameter tuning job by setting any of your tunable hyperparameters to specific values. Also, you will want to maximize the auc evaluation metric, not minimize it.
Option B is correct. Setting the values of your tunable hyperparameters to ranges of values allows your hyperparameter tuning jobs to use either bayesian or random search to find the best combination of values. Also, maximizing the auc optimization metric is a proven approach to reaching the optimal set of tunable hyperparameters.
Option C is incorrect. Setting the values of your tunable hyperparameters to ranges of values allows your hyperparameter tuning jobs to use either bayesian or random search to find the best combination of values. However, choosing to minimize ndcg as your optimization metric will result in a suboptimal result. The ndcg metric is supposed to be maximized, not minimized.
Option D is incorrect. Setting the values of your tunable hyperparameters to ranges of values allows your hyperparameter tuning jobs to use either bayesian or random search to find the best combination of values. However, running only one hyperparameter tuning job will not give you the optimal result. You need to run several training jobs to get to the best set of tunable hyperparameters in a reasonable amount of time.
References:
Please see the AWS SageMaker developer guide titled Perform Automatic Model Tuning (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html),
The AWS SageMaker developer guide titled Tune an XGBoost Model (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html),
The AWS SageMaker developer guide titled How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html)