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Answer Posted / Raja Babu Singh
Handling imbalanced datasets can be challenging, as models trained on such data may perform poorly for the minority class. Techniques to address this include oversampling the minority class, undersampling the majority class, or using a combination of both (SMOTE). Another approach is cost-sensitive learning, which gives higher weight to instances from the minority class during training.
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