sklearn.metrics.jaccard_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn') [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_true1d array-like, or label indicator array / sparse matrix 

Ground truth (correct) labels.  
y_pred1d array-like, or label indicator array / sparse matrix 

Predicted labels, as returned by a classifier.  
labelsarray-like of shape (n_classes,), default=None 

The set of labels to include when average != 'binary', and their order if average 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 in y_true and y_pred are used in sorted order.  
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; setting labels=[pos_label] and average != 'binary' will report scores for that label only.  
average{None, ‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’}, default=’binary’ 

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_weightarray-like of shape (n_samples,), default=None 

Sample weights.  
zero_division“warn”, {0.0, 1.0}, default=”warn” 

Sets the value to return when there is a zero division, i.e. when there there are no negative values in predictions and labels. If set to “warn”, this acts like 0, but a warning is also raised.    Returns 
 
scorefloat (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...])