The F1 measure is the harmonic mean, or weighted average, of the precision and recall scores. Also called the f-measure or the f-score, the F1 score is calculated using the following formula:

The F1 measure penalizes classifiers with imbalanced precision and recall scores, like the trivial classifier that always predicts the positive class. A model with perfect precision and recall scores will achieve an F1 score of one. A model with a perfect precision score and a recall score of zero will achieve an F1 score of zero. As for precision and recall, scikit-learn provides a function to calculate the F1 score for a set of predictions. Let's compute our classifier's F1 score. The following snippet continues the previous code sample:

>>> f1s = cross_val_score(classifier, X_train, y_train, cv=5, scoring='f1')
>>> print 'F1', np.mean(f1s), f1s

F1 0.80261302628 [ 0.82539683  0.8         0.77348066  0.83157895  0.7826087 ]

The arithmetic mean of our classifier's precision and recall scores is 0.803. As the difference between the classifier's precision and recall is small, the F1 measure's penalty is small. Models are sometimes evaluated using the F0.5 and F2 scores, which favor precision over recall and recall over precision, respectively.