A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameters that will generate the most accurate model.
Which approach will meet these requirements with the LEAST operational overhead?

A. Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm.

B. Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.

C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.

D. Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.








 

Suggested Answer: C

Community Answer: C




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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