MLS-C01 · Question #340
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
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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?
Options
- ATune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
- BTune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using
- CTune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
- DTune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using
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