F-score (F1 Score)
A performance metric that combines precision and recall into a single score, providing a balanced measure of classification accuracy.
What is the F-score (F1 Score)?
The F1 Score is calculated as the harmonic mean of precision and recall. Precision measures how many predicted positives are correct, while recall measures how many actual positives the model successfully identifies. The F1 Score ranges from 0 to 1, with higher values indicating a better balance between precision and recall.
Why is the F-score Important?
Accuracy alone can be misleading when classes are imbalanced. The F1 Score considers both false positives and false negatives, making it especially useful when both types of errors matter. It provides a single metric for comparing classification models while accounting for their ability to identify positive cases accurately.
Common use cases
The F1 Score is commonly used in fraud detection, spam filtering, medical diagnosis, information retrieval, cybersecurity, and other classification tasks.