sklearn.metrics.jaccard_similarity_score — scikit-learn 0.20.4 documentation (original) (raw)
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: | y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, 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 : array-like of shape = [n_samples], optional Sample weights. |
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| 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 with normalize == False. |
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 |
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Examples
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