Example Concept: Imbalanced datasets can be addressed by resampling methods like oversampling the minority class or undersampling the majority class. Additionally, techniques such as using synthetic data generation (e.g., SMOTE), adjusting class weights in algorithms, or employing ensemble methods like boosting can help. Evaluation metrics like precision, recall, and the F1-score are preferred over accuracy to better assess model performance on imbalanced data.