Example Concept: Imbalanced datasets can be managed using techniques such as oversampling the minority class (e.g., SMOTE), undersampling the majority class, or employing ensemble methods like Random Forests that are inherently more robust to class imbalance. Additionally, adjusting class weights in algorithms like logistic regression or using specialized algorithms like XGBoost with built-in handling for imbalance can improve model performance. Evaluation metrics such as precision-recall curves or the F1 score are preferred over accuracy to better capture the model's ability to handle imbalanced data.