AWS Certified Machine Learning - Specialty MLS-C01 — Topic 1 Question 313

Question

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?

Accepted Answer

Correct answer: B. The correct answer is B. Option 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"}}. This option delivers the requirement at the lowest cost.

AWS Certified Machine Learning - Specialty MLS-C01 — Question 313