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Customer churn is often a rare event with a low occurrence frequency. What is an appropriate way to handle class imbalance during training?
①Balance the classes using class weighting or synthetic samples such as SMOTE.
②Train using only the majority class, ignoring positive examples.
③Always perform simple random undersampling.
④Evaluation is based solely on accuracy.
⑤Assume that imbalance is not a problem.