{"id":3989,"date":"2025-07-24T13:33:23","date_gmt":"2025-07-24T13:33:23","guid":{"rendered":"https:\/\/uplatz.com\/blog\/?p=3989"},"modified":"2025-07-24T13:33:23","modified_gmt":"2025-07-24T13:33:23","slug":"f1-score-formula-balancing-precision-and-recall-in-one-metric","status":"publish","type":"post","link":"https:\/\/uplatz.com\/blog\/f1-score-formula-balancing-precision-and-recall-in-one-metric\/","title":{"rendered":"F1 Score Formula \u2013 Balancing Precision and Recall in One Metric"},"content":{"rendered":"

\"\"\ud83d\udd39 Short Description:<\/b>
\n<\/b> The F1 Score combines precision and recall into a single metric to evaluate classification models, especially when classes are imbalanced.<\/span><\/p>\n

\ud83d\udd39 Description (Plain Text):<\/b><\/p>\n

The <\/span>F1 Score formula<\/b> is a widely used metric that brings <\/span>precision<\/b> and <\/span>recall<\/b> together into a single harmonic mean. It provides a balanced measure that considers both false positives and false negatives, making it incredibly useful in <\/span>imbalanced datasets<\/b> where accuracy can be misleading.<\/span><\/p>\n

Formula:<\/b>
\n<\/b> F1 Score = 2 \u00d7 (Precision \u00d7 Recall) \/ (Precision + Recall)<\/b><\/p>\n

This formula ensures that both precision and recall have equal influence on the final score. If either precision or recall is low, the F1 Score will also be low. This makes it a reliable metric when you care about both <\/span>catching positives<\/b> (recall) and <\/span>not raising too many false alarms<\/b> (precision).<\/span><\/p>\n

Example:<\/b>
\n<\/b> Let\u2019s say a fraud detection model has:<\/span><\/p>\n