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F-score (also known as F1-score) is a metric used to evaluate the performance of a binary classification model. It is a harmonic mean of precision and recall.
Precision is the ratio of true positives to the total number of positive predictions, while recall is the ratio of true positives to the total number of actual positive instances in the data.
The F-score is calculated by taking the harmonic mean of precision and recall. It is a way to balance the trade-off between precision and recall.
The formula for F-score is:
F-score = 2 * (precision * recall) / (precision + recall)
The F-score ranges from 0 to 1, with 1 being the best score. A high F-score indicates that the model has high precision and recall and is performing well in correctly identifying positive instances. « Back to Glossary Index