The F1 score is a metric used to evaluate the performance of a classification model. It balances two key aspects:
- Precision: the proportion of true positive predictions out of all positive predictions made.
- Recall: the proportion of true positive predictions out of all actual positive cases.
The F1 score is the harmonic mean of precision and recall:
It’s especially useful when you need a balance between precision and recall, and when classes are imbalanced.
