What are different evaluation metrics used for classification models?

Common evaluation metrics for classification models include accuracy, precision, recall, F1 score, and specificity. - **Accuracy** measures the proportion of true results (both true positives and true negatives) among the total number of cases examined. - **Precision** indicates the proportion of true positive results in all positive predictions, focusing on the relevance of the positive class. - **Recall** (or sensitivity) measures the proportion of actual positives correctly identified by the model. - **F1 Score** is the harmonic mean of precision and recall, providing a balance between the two. - **Specificity** measures the proportion of actual negatives correctly identified. The choice of metric often depends on the specific business case or problem being addressed.