What’s the best way to handle highly imbalanced datasets during model training?

Example Concept: One common approach to handle imbalanced datasets is to use resampling techniques like oversampling the minority class (e.g., SMOTE) or undersampling the majority class. Additionally, using evaluation metrics like precision, recall, and F1-score instead of accuracy can provide a better understanding of model performance. Algorithms like Random Forests or Gradient Boosting can also be tuned to handle class imbalance by adjusting class weights.