I’m still learning this, but from what I’ve understood, handling imbalanced datasets is less about just fixing the data and more about choosing the right approach based on the problem.

Some things that seem to work:

• Resampling techniques like oversampling (SMOTE) or undersampling to balance classes
• Using appropriate metrics like F1-score, precision-recall instead of just accuracy
• Class weights in models so the minority class gets more importance
• Trying ensemble methods that are more robust to imbalance

Also, I’ve noticed that sometimes balancing too aggressively can lead to overfitting, especially with synthetic data.

Would love to know how others decide between resampling vs just adjusting the model