MLS-C01 · Question #262
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
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Question
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?
Options
- AApply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the
- BApply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the
- CUndersample the minority class.
- DOversample the majority class.
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