The F1 Score, also called the F score or F measure, is a measure of a test’s accuracy. It is calculated from the precision and recall of the test, where the precision is the number of correctly identified positive results divided by the number of all positive results, including those not identified correctly, and the recall is the number of correctly identified positive results divided by the number of all samples that should have been identified as positive.

The F1 score is defined as the weighted harmonic mean of the test’s precision and recall. This score is calculated according to the formula.

Formula:

Necessary: F-Measure provides a single score that balances both the concerns of precision and recall in one number.

A good F1 score means that you have low false positives and low false negatives, so you’re correctly identifying real threats, and you are not disturbed by false alarms.

An F1 score is considered perfect when it’s 1, while the model is a total failure when it’s 0.

F1 Score is a better metric to evaluate our model on real-life classification problems and when imbalanced class distribution exists.