8.19.1.7. 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 case, this is the weighted average of the F1 score of each class.

Parameters:

y_true : array, shape = [n_samples]

Ground truth (correct) target values.

y_pred : array, shape = [n_samples]

Estimated targets as returned by a classifier.

labels : array

Integer array of labels.

pos_label : int

In the binary classification case, give the label of the positive class (default is 1). Everything else but pos_label is considered to belong to the negative class. Set to None in the case of multiclass classification.

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

In the multiclass classification case, this determines the type of averaging performed on the data.

None:

Do not perform any averaging, return the score for each class.

‘macro’:

Average over classes (does not take imbalance into account).

‘micro’:

Average over instances (takes imbalance into account). This implies that precision == recall == F1.

‘weighted’:

Average weighted by support (takes imbalance into account). Can result in F-score that is not between precision and recall.

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

http://en.wikipedia.org/wiki/F1_score

Examples

In the binary case:

>>> from sklearn.metrics import f1_score
>>> y_pred = [0, 1, 0, 0]
>>> y_true = [0, 1, 0, 1]
>>> f1_score(y_true, y_pred)  
0.666...

In the multiclass case:

>>> 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. ])