F1 Score

The F1 score is the harmonic mean of recall and precision.

The industry commonly uses the F1 score (the harmonic mean of precision and recall) to comprehensively evaluate terminology recognition performance.

The F1 score is a special case of the Fβ score when β=1.

In multi-class tasks, accuracy becomes distorted when class samples are imbalanced; precision reflects the degree of false positives, recall reflects the degree of false negatives, and the F1 score is the harmonic mean of precision and recall.

When the proportion of diseased samples is low, recall and F1 score reflect the model's actual detection capability better than accuracy.

Evaluation metrics suitable for the systematic review screening scenario include recall, precision, F1 score, and Work Saved over Sampling (WSS).

The F1 score is the harmonic mean of precision and recall; when the two are weighted differently, the Fβ score can be used to reflect priorities.

The F1 score uses the harmonic mean rather than the arithmetic mean because the harmonic mean is more sensitive to smaller values, so F1 is only high when both precision and recall are simultaneously high.

In imbalanced classification problems, a model can achieve very high accuracy even by predicting all samples as the majority class, so it is necessary to shift to more discriminative metrics such as precision-recall curves, F1 score, Matthews correlation coefficient, or AUC-ROC

When dealing with highly imbalanced classification datasets, one should consider SMOTE oversampling or shifting the evaluation metric from accuracy to F1 score.

In a binary classification confusion matrix, the F1 score is the harmonic mean of precision and recall.

In class-imbalanced scenarios, the F1 score better reflects the model's ability to recognize the minority class than accuracy does.