F1 score, also known as the F-measure or F-score, is a measure of a test’s accuracy. It is a combination of precision and recall. The precision expresses the percentage of correctly predicted positive results among all positive predictions, while recall expresses the percentage of correctly predicted positive results among all relevant samples. The F1 score is the harmonic mean of precision and recall.

The F1 score is used to evaluate a model’s performance in a binary classification task such as spam filtering. It complements precision and recall, which are also used in the evaluation of a model. The F1 score is the most commonly used measure among all available accuracy measures for binary classification.

To calculate the F1 score, there are two terms—precision and recall—which are calculated as:

● Precision = (True Positives) / (True Positives + False Positives)
● Recall = (True Positives) / (True Positives + False Negatives)

The F1 score is then calculated as:

F1 Score = 2 (Precision x Recall) / (Precision + Recall)

The F1 score combines precision and recall into one measure. However, it is not always the best choice, as its value can be non-existing if one of the two terms is zero (as in the case of a perfect precision and a perfect recall where one will be zero). In such cases, the Föbeta score should be used instead.

In conclusion, the F1 score is a measure of a classification model’s accuracy. It is calculated by combining precision and recall and is most widely used as an accuracy measure for binary classification problems.

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