/scikit-learn

# sklearn.metrics.jaccard_similarity_score

`sklearn.metrics.jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None)` [source]

Jaccard similarity coefficient score

The Jaccard index , 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. `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.

#### 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
```

## Examples using `sklearn.metrics.jaccard_similarity_score` Classifier Chain