sklearn.metrics
.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, settingaverage='binary'
will return the Jaccard similarity coefficient forpos_label
. Ifaverage
is not'binary'
,pos_label
is ignored and scores for both classes are computed, then averaged or both returned (whenaverage=None
). Similarly, for multiclass and multilabel targets, scores for alllabels
are either returned or averaged depending on theaverage
parameter. Uselabels
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 ifaverage 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 iny_true
andy_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, setlabels=[pos_label]
andaverage != '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 notNone
, 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]) 0.6666...
In the 2D comparison case (e.g. image similarity):
>>> jaccard_score(y_true, y_pred, average="micro") 0.6
In the multilabel case:
>>> jaccard_score(y_true, y_pred, average='samples') 0.5833... >>> jaccard_score(y_true, y_pred, average='macro') 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...])
Examples using sklearn.metrics.jaccard_score
¶
Multilabel classification using a classifier chain