Example Concept: Resampling techniques like oversampling the minority class (e.g., SMOTE) or undersampling the majority class can help balance the dataset. Algorithmic adjustments, such as using cost-sensitive learning or ensemble methods like Random Forests, can also improve performance. Additionally, choosing appropriate evaluation metrics like the F1-score, precision-recall curve, or area under the ROC curve (AUC-ROC) is essential for assessing model performance on imbalanced data.