What Metrics are Used to Evaluate Binary Classifiers?
Several metrics are used to evaluate the performance of binary classifiers, including:
Accuracy: The proportion of true results (both true positives and true negatives) among the total number of cases examined. Sensitivity (or recall): The ability of the test to correctly identify those with the disease (true positive rate). Specificity: The ability of the test to correctly identify those without the disease (true negative rate). Precision: The proportion of positive identifications that are actually correct (also known as positive predictive value). ROC Curve: A graphical representation of a classifier's performance across different threshold settings.
