sklearn.metrics
.recall_score¶
- sklearn.metrics.recall_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[source]¶
Compute the recall.
The recall is the ratio
tp / (tp + fn)
wheretp
is the number of true positives andfn
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.
Read more in the User Guide.
- Parameters
- y_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
- y_pred1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
- labelsarray-like, default=None
The set of labels to include when
average != 'binary'
, and their order ifaverage is None
. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels iny_true
andy_pred
are used in sorted order.Changed in version 0.17: Parameter
labels
improved for multiclass problem.- pos_labelstr or int, default=1
The class to report if
average='binary'
and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.- average{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’
This parameter is required for multiclass/multilabel targets. If
None
, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:'binary'
:Only report results for the class specified by
pos_label
. This is applicable only if targets (y_{true,pred}
) are binary.'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. Weighted recall is equal to accuracy.
'samples'
:Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from
accuracy_score
).
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- zero_division“warn”, 0 or 1, default=”warn”
Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised.
- Returns
- recallfloat (if average is not None) or array of float of 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.
See also
precision_recall_fscore_support
Compute precision, recall, F-measure and support for each class.
precision_score
Compute the ratio
tp / (tp + fp)
wheretp
is the number of true positives andfp
the number of false positives.balanced_accuracy_score
Compute balanced accuracy to deal with imbalanced datasets.
multilabel_confusion_matrix
Compute a confusion matrix for each class or sample.
PrecisionRecallDisplay.from_estimator
Plot precision-recall curve given an estimator and some data.
PrecisionRecallDisplay.from_predictions
Plot precision-recall curve given binary class predictions.
Notes
When
true positive + false negative == 0
, recall returns 0 and raisesUndefinedMetricWarning
. This behavior can be modified withzero_division
.Examples
>>> 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.]) >>> y_true = [0, 0, 0, 0, 0, 0] >>> recall_score(y_true, y_pred, average=None) array([0.5, 0. , 0. ]) >>> recall_score(y_true, y_pred, average=None, zero_division=1) array([0.5, 1. , 1. ]) >>> # multilabel classification >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]] >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]] >>> recall_score(y_true, y_pred, average=None) array([1. , 1. , 0.5])