Why use Area under the curve? (AUC - ROC)

Leonard Dieguez

In scenarios with imbalanced datasets, ROC curves and AUC-ROC scores are valuable tools for assessing and comparing the performance of machine learning classifiers. They help provide insights into a model's ability to distinguish between classes and can guide decision-making regarding threshold selection.