F1

A "roll-up" binary classification metric that relies on both precision and recall. Here is the formula:

Suppose precision and recall have the following values: - precision = 0.6 - recall = 0.4 You calculate F1 as follows:

When precision and recall are fairly similar (as in the preceding example), F1 is close to their mean. When precision and recall differ significantly, F1 is closer to the lower value. For example: - precision = 0.9 - recall = 0.1

Practitioners refer to f1 when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.