Evaluating Binary Classifiers on Imbalanced Datasets: Why AUC-PR Beats AUC-ROC and Gini
AUC-ROC and Gini are popular metrics for evaluating binary classifiers, but they can be misleading on imbalanced datasets. Discover why AUC-PR, with its focus on Precision and Recall, offers a better evaluation for handling rare events.