What are the existing classification quality metrics?

Metrics often used to evaluate the quality of binary classification include:

• Accuracy — the proportion of correctly classified objects (both positive and negative) out of the total.
• Precision — the proportion of correctly predicted positive objects among all objects predicted as positive by the model.
• Recall — the proportion of correctly identified positive objects out of all actual positive objects.
• F1-score — the harmonic mean of precision and recall, balancing between them.
• ROC-AUC — the area under the ROC curve, indicating the quality of class separation at different thresholds.

For example, if the model predicts a disease, precision shows how often the positive prediction is correct, and recall indicates how many actual cases it can detect.