8.19.1.14. sklearn.metrics.recall_score

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

Compute the recall

The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.

The best value is 1 and the worst value is 0.

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 scores 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:

recall : float (if average is not None) or array of float, shape = [n_unique_labels]

Recall of the positive class in binary classification or weighted average of the recall of each class for the multiclass task.

Examples

In the binary case:

>>> from sklearn.metrics import recall_score
>>> y_pred = [0, 1, 0, 0]
>>> y_true = [0, 1, 0, 1]
>>> recall_score(y_true, y_pred)
0.5

In the multiclass case:

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