How to measure the quality of a binary classification model? What metrics do you know?

Different metrics are used to evaluate the quality of a binary classification model, reflecting various aspects of the model's performance:

• Accuracy — the proportion of correct predictions (both positive and negative) out of the total.
• Precision — how many of the predicted positives are actually positive.
• Recall — how many of the actual positives the model correctly identified.
• 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.

The choice of metric depends on the task. For example, in disease detection, recall is important to avoid missing sick patients, while in spam detection, precision is crucial to avoid blocking legitimate emails.