How Can Evaluation Metrics Be Adjusted for Imbalance?
Standard evaluation metrics like accuracy can be misleading in imbalanced datasets. Alternative metrics such as precision, recall, F1-score, and AUC-ROC are better suited for evaluating models in the presence of class imbalance. These metrics provide a clearer picture of a model's ability to correctly identify the minority class.
