MLS-C01 Web TestEngine Demo

Q49 Single choice

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:

1. True positive rate (TPR): 0.700
2. False negative rate (FNR): 0.300
3. True negative rate (TNR): 0.977

4. False positive rate (FPR): 0.023
5. Overall accuracy: 0.949

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

A
Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
B
Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
C
Undersample the minority class.
D
Oversample the majority class.

MLS-C01 Web TestEngine demo