Mastering Machine Learning with scikit-learn - Second Edition

Calculating the F1 measure

The F1 measure is the harmonic mean of the precision and recall scores. 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 1. A model with a perfect precision score and a recall score of 0 will achieve an F1 score of 0. Let's compute our classifier's F1 score:

# In[3]:f1s = cross_val_score(classifier, X_train, y_train, cv=5,  scoring='f1')print('F1 score: %s' % np.mean(f1s))# Out[3]:F1 score: 0.809067846627

Models are sometimes evaluated using the F0.5 and F2 scores, which bias precision over recall and recall over precision, respectively. ...