The F1 score is a way to combine two important metrics, precision and recall, into a single value.

Precision is the proportion of correctly identified positive cases out of all predicted positive cases. While Recall is the proportion of correctly identified positive cases out of all actual positive cases.

The F1 score takes both metrics into account to give you the overall performance score of the model or classifier. It is the harmonic mean of precision and recall, meaning it gives more weight to the lower of the two values. The F1 score values range between zero and one, with a higher score indicating better model performance.

In simple terms, the F1 score is the measure of the model's accuracy, which takes into account both the model's ability to identify relevant data (recall) and the model's ability to not include irrelevant data, what the accuracy of the results is.

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