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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…

The correct answer is D. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using. F1 metric combines both precision and recall which is more suitable for unbalanced datasets.

Modeling

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

How the community answered

(18 responses)
  • A
    22% (4)
  • B
    17% (3)
  • C
    6% (1)
  • D
    56% (10)

Explanation

F1 metric combines both precision and recall which is more suitable for unbalanced datasets.

Topics

#XGBoost Hyperparameter Tuning#Imbalanced Data Handling#Automatic Model Tuning (AMT)#Recall Optimization

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