sklearn.metrics.jaccard_similarity_score(y_true, y_pred, normalize=True, 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: 


Returns: 

See also
In binary and multiclass classification, this function is equivalent to the accuracy_score
. It differs in the multilabel classification problem.
[1]  Wikipedia entry for the Jaccard index 
>>> import numpy as np >>> from sklearn.metrics import jaccard_similarity_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> jaccard_similarity_score(y_true, y_pred) 0.5 >>> jaccard_similarity_score(y_true, y_pred, normalize=False) 2
In the multilabel case with binary label indicators:
>>> jaccard_similarity_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.75
sklearn.metrics.jaccard_similarity_score
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