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In a multi-class classification problem, you have trained an ensemble of models using different algorithms. How can you handle the class imbalance issue in the ensemble, and explain the impact of class imbalance on the ensemble's performance?
A
Use resampling techniques to balance the class distribution before training each model in the ensemble.
B
Assign different weights to the classes in the loss function during training, based on their frequency in the dataset.
C
Combine the predictions of the ensemble models using a voting scheme that gives more importance to the minority class.
D
All of the above
