Example Concept: To evaluate the impact of data imbalance, you can use metrics such as precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), which provide insights into model performance beyond accuracy. Additionally, techniques like resampling (oversampling the minority class or undersampling the majority class), using synthetic data generation methods like SMOTE, or employing algorithms that are robust to imbalance (e.g., decision trees, ensemble methods) can help mitigate the effects of imbalance.