Evaluation Metrics for Classification Models

Once a classification model makes predictions, you need clear metrics to judge its performance.

• Accuracy: The percentage of total predictions the model got correct.
• Precision: Out of all positive predictions made, how many were actually correct.
• Recall: Out of all actual positive cases, how many the model correctly identified.
• F1 Score: A balance between precision and recall, useful when both matter equally.
• Specificity: How well the model correctly identifies negative cases.