Le score F1 est la moyenne harmonique de la précision et du rappel, utilisée pour évaluer les performances de classification et d’extraction d’information. Il varie de 0 à 1, des valeurs plus élevées indiquant un meilleur équilibre entre les positifs correctement identifiés et les positifs manqués.
In classification , an evaluation metric that balances precision and recall .
The F1 score is a metric used to evaluate the accuracy of a model's output. It's particularly useful for assessing the performance of models on tasks where both precision and recall are important, such as information extraction. For generative AI models, the F1 score can be used to compare the model's inferences with ground truth data to determine the model's accuracy. However, for generative tasks like summarization and text generation, other metrics like Rough-L score might be more appropriate.
A side-by-side comparison of Accuracy and F1 Score. Understand how overall correctness differs from a balanced measure of precision and recall.
A side-by-side comparison of F1 Score and Area Under the ROC Curve. Understand how a threshold-dependent balance of precision and recall differs from ranking discrimination across thresholds.