Examtopics

AWS Certified Machine Learning - Specialty

  • Topic 1 Question 234

    A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:

    • True positive rate (TPR): 0.700 • False negative rate (FNR): 0.300 • True negative rate (TNR): 0.977 • False positive rate (FPR): 0.023 • Overall accuracy: 0.949

    Which solution should the data scientist use to improve the performance of the model?

    • Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

    • Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

    • Undersample the minority class.

    • Oversample the majority class.

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