The F-score, also called the F1-score, is a measure of a model’s accuracy on a dataset. It is used to evaluate binary classification systems, which classify examples into ‘positive’ or ‘negative’.

The F-score is a way of combining the precision and recall of the model, and it is defined as the harmonic mean of the model’s precision and recall.

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