MLS-C01 · Question #365
A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset
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Question
A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank. A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions. Which solution will meet these requirements?
Options
- AApply anomaly detection to remove outliers from the training dataset before training.
- BApply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.
- CApply normalization to the features of the training dataset before training.
- DApply undersampling to the training dataset before training.
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