F1 Score Calculator
Calculate the F1 score from precision and recall values. The F1 score is the harmonic mean of precision and recall, used to evaluate classification model performance.
How to Use the F1 Score Calculator
- Enter the Precision value (between 0 and 1).
- Enter the Recall value (between 0 and 1).
- Click Calculate to get the F1 score.
Cas d'utilisation
- •Evaluating NLP models such as named entity recognition.
- •Assessing image classification performance.
- •Comparing models trained on imbalanced datasets.
- •Reporting metrics in machine learning competitions.
Formule
F1 = 2 × Precision × Recall / (Precision + Recall). Values range from 0 (worst) to 1 (best).
Questions fréquemment posées
What is the F1 score?
The F1 score is the harmonic mean of precision and recall, balancing both metrics into a single value.
What is a good F1 score?
An F1 score above 0.8 is generally considered good. The ideal is 1.0.
When is F1 score more useful than accuracy?
F1 is more useful on imbalanced datasets where one class is much more frequent than the other.