sklearn.metrics
.jaccard_score¶

sklearn.metrics.
jaccard_score
(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)[source]¶ Jaccard similarity coefficient score
The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in
y_true
.Read more in the User Guide.
Parameters:  y_true : 1d arraylike, or label indicator array / sparse matrix
Ground truth (correct) labels.
 y_pred : 1d arraylike, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
 labels : list, optional
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. pos_label : str or int, 1 by default
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 : string, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’]
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.
'samples'
:Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
 sample_weight : arraylike of shape = [n_samples], optional
Sample weights.
Returns:  score : float (if average is not None) or array of floats, shape = [n_unique_labels]
See also
accuracy_score
,f_score
,multilabel_confusion_matrix
Notes
jaccard_score
may be a poor metric if there are no positives for some samples or classes. Jaccard is undefined if there are no true or predicted labels, and our implementation will return a score of 0 with a warning.References
[1] Wikipedia entry for the Jaccard index Examples
>>> import numpy as np >>> from sklearn.metrics import jaccard_score >>> y_true = np.array([[0, 1, 1], ... [1, 1, 0]]) >>> y_pred = np.array([[1, 1, 1], ... [1, 0, 0]])
In the binary case:
>>> jaccard_score(y_true[0], y_pred[0]) 0.6666...
In the multilabel case:
>>> jaccard_score(y_true, y_pred, average='samples') 0.5833... >>> jaccard_score(y_true, y_pred, average='macro') 0.6666... >>> jaccard_score(y_true, y_pred, average=None) array([0.5, 0.5, 1. ])
In the multiclass case:
>>> y_pred = [0, 2, 1, 2] >>> y_true = [0, 1, 2, 2] >>> jaccard_score(y_true, y_pred, average=None) ... array([1. , 0. , 0.33...])