In a multiclass classification problem with imbalanced classes, how would you ensure that your model is not biased towards the majority class?
- Implement resampling techniques and consider using balanced algorithms
- Increase the number of features
- Use only majority class for training
- Use the same algorithm for all classes
Implementing resampling techniques to balance the classes and considering algorithms that handle class imbalance can ensure that the model doesn't become biased towards the majority class.
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