What is the impact of undersampling on model performance?
Undersampling can help reduce overfitting by balancing class distribution, but it may also lead to loss of important data.
Can you explain how it affects accuracy?
While undersampling can improve model training speed and reduce complexity, it might decrease accuracy if critical samples are removed.
Are there any alternatives to undersampling?
Yes, alternatives include oversampling, SMOTE (Synthetic Minority Over-sampling Technique), and using ensemble methods.
How do I decide whether to use undersampling or oversampling?
It depends on your dataset size, class imbalance, and the importance of minority class samples. Experimentation is key.
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