Handling Class Imbalance with SMOTE

Hard

machine-learning Capital One Stripe JPMorgan

Multiple Choice

SMOTE is not effective for class imbalance problems SMOTE should only be applied to the training set after splitting to avoid leaking information into the test set SMOTE created too many synthetic samples, causing the model to overfit The production data has a different feature distribution unrelated to SMOTE

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