Example Concept: One common approach to handle imbalanced datasets is to use resampling techniques. Oversampling methods like SMOTE (Synthetic Minority Over-sampling Technique) create synthetic examples of the minority class, while undersampling reduces the majority class size. Additionally, using evaluation metrics such as F1-score, precision-recall curves, and area under the precision-recall curve (AUC-PR) can provide a more balanced view of model performance compared to accuracy alone. Algorithmic techniques like cost-sensitive learning or using ensemble methods such as Random Forests and Gradient Boosting can also improve model performance on imbalanced data.