MLS-C01 Question #262: Real Exam Question with Answer & Explanation

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.