An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items.
A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.
How should the data scientist meet these requirements MOST cost-effectively?

A. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:accuracy", "Type": "Maximize"}}.

B. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation'll", "Type": "Maximize"}}.

C. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.

D. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Minimize"}}.








 

Suggested Answer: C

Community Answer: B




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


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