8.17.1.5. 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 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]

True targets

y_pred : array, shape = [n_samples]

Predicted targets

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.

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 f1 score that is not between precision and recall.

Returns :

recall : float

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