jaccard_score#

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.

Support beyond term:binary targets is achieved by treating multiclass and multilabel data as a collection of binary problems, one for each label. For the binary case, setting average='binary' will return the Jaccard similarity coefficient for pos_label. If average is not 'binary', pos_label is ignored and scores for both classes are computed, then averaged or both returned (when average=None). Similarly, for multiclass and multilabel targets, scores for all labels are either returned or averaged depending on the average parameter. Use labels specify the set of labels to calculate the score for.

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 in multiclass classification to exclude a “negative class”. Labels not present in the data can be included and will be “assigned” 0 samples. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order.

pos_labelint, float, bool or str, default=1

The class to report if average='binary' and the data is binary, otherwise this parameter is ignored. For multiclass or multilabel targets, set labels=[pos_label] and average != 'binary' to report metrics for one label only.

average{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, 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 or ndarray of shape (n_unique_labels,), dtype=np.float64

The Jaccard score. When average is not None, a single scalar is returned.

See also

accuracy_score

Function for calculating the accuracy score.

f1_score

Function for calculating the F1 score.

multilabel_confusion_matrix

Function for computing a confusion matrix for each class or sample.

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

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])
np.float64(0.6666...)

In the 2D comparison case (e.g. image similarity):

>>> jaccard_score(y_true, y_pred, average="micro")
np.float64(0.6)

In the multilabel case:

>>> jaccard_score(y_true, y_pred, average='samples')
np.float64(0.5833...)
>>> jaccard_score(y_true, y_pred, average='macro')
np.float64(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...])
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