What are the best practices for handling imbalanced data in Machine Learning?
The question is about Machine Learning .
The best ways to handle imbalanced data in Machine Learning include using resampling techniques like oversampling the minority class or undersampling the majority class to even out the data. You can also apply specialised algorithms like SMOTE to make the classes more balanced. It's useful to employ evaluation metrics like precision-recall curves instead of just accuracy, as they provide a clearer view of model performance with imbalanced data.
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