Which solution will meet these requirements?

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In: MLS-C01
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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?

Apply anomaly detection to remove outliers from the training dataset before training.

Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.

Apply normalization to the features of the training dataset before training.

Apply undersampling to the training dataset before training.

Explanations:

Anomaly detection does not address the class imbalance, which is likely causing prediction issues.

SMOTE generates synthetic samples for the minority class, addressing class imbalance in the dataset.

Normalization only scales feature values; it does not address the issue of class imbalance.

Undersampling reduces the majority class, potentially losing important information and worsening model performance.

2025-10-05

1 Comment

  1. Olivia
    Author

    From what I’ve heard, the answer is:
    Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.

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