sklearn.metrics
.jaccard_similarity_score¶
Warning
DEPRECATED

sklearn.metrics.
jaccard_similarity_score
(y_true, y_pred, normalize=True, sample_weight=None)[source]¶ Jaccard similarity coefficient score
Deprecated since version 0.21: This is deprecated to be removed in 0.23, since its handling of binary and multiclass inputs was broken.
jaccard_score
has an API that is consistent with precision_score, f_score, etc.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.
 normalize : bool, optional (default=True)
If
False
, return the sum of the Jaccard similarity coefficient over the sample set. Otherwise, return the average of Jaccard similarity coefficient. sample_weight : arraylike of shape = [n_samples], optional
Sample weights.
Returns:  score : float
If
normalize == True
, return the average Jaccard similarity coefficient, else it returns the sum of the Jaccard similarity coefficient over the sample set.The best performance is 1 with
normalize == True
and the number of samples withnormalize == False
.
See also
Notes
In binary and multiclass classification, this function is equivalent to the
accuracy_score
. It differs in the multilabel classification problem.References
[1] Wikipedia entry for the Jaccard index