In a scenario where the targets are imbalanced, how would this affect the training and testing process, and what strategies would you apply to handle it?
- Apply resampling techniques
- Focus on specific evaluation metrics
- Ignore the imbalance
- Use only the majority class
Imbalanced targets can bias the model towards the majority class, leading to poor performance on the minority class. Applying resampling techniques like oversampling the minority class or undersampling the majority class balances the data. This, combined with using appropriate evaluation metrics like precision, recall, or F1 score, ensures that the model is more sensitive to the minority class.
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