sklearn.metrics.f1_score

sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='weighted')

Compute the F1 score, also known as balanced F-score or F-measure

The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:

F1 = 2 * (precision * recall) / (precision + recall)

In the multi-class and multi-label case, this is the weighted average of the F1 score of each class.

Parameters :

y_true : array-like or list of labels or label indicator matrix

Ground truth (correct) target values.

y_pred : array-like or list of labels or label indicator matrix

Estimated targets as returned by a classifier.

labels : array

Integer array of labels.

pos_label : str or int, 1 by default

If average is not None and the classification target is binary, only this class’s scores will be returned.

average : string, [None, ‘micro’, ‘macro’, ‘samples’, ‘weighted’ (default)]

If None, the scores for each class are returned. Otherwise, unless pos_label is given in binary classification, this determines the type of averaging performed on the data:

'micro':

Calculate metrics globally by counting the total true positives, false negatives and false positives.

'macro':

Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

'weighted':

Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.

'samples':

Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).

Returns :

f1_score : float or array of float, shape = [n_unique_labels]

F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.

References

[R158]

Wikipedia entry for the F1-score

Examples

>>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> f1_score(y_true, y_pred, average='macro')  
0.26...
>>> f1_score(y_true, y_pred, average='micro')  
0.33...
>>> f1_score(y_true, y_pred, average='weighted')  
0.26...
>>> f1_score(y_true, y_pred, average=None)
array([ 0.8,  0. ,  0. ])