Example Concept: Class imbalance can be managed using methods like oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique), undersampling the majority class, or using ensemble methods like Random Forests that are less sensitive to imbalance. Additionally, algorithmic approaches such as cost-sensitive learning adjust the model's loss function to penalize misclassification of the minority class more heavily. Evaluation metrics like precision-recall curves or the F1-score are preferred over accuracy in imbalanced scenarios to provide a more accurate assessment of model performance.