# Jaccard¶

Jaccard index for binary multi-outputs.

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 the set of predicted labels for a sample with the corresponding set of labels in y_true.

The Jaccard index may be a poor metric if there are no positives for some samples or labels. The Jaccard index is undefined if there are no true or predicted labels, this implementation will return a score of 0.0 if this is the case.

## Parameters¶

• cm (river.metrics.confusion.MultiLabelConfusionMatrix) – defaults to None

This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.

## Attributes¶

• bigger_is_better

Indicate if a high value is better than a low one or not.

• requires_labels

• sample_correction

• works_with_weights

Indicate whether the model takes into consideration the effect of sample weights

## Examples¶

>>> from river import metrics

>>> y_true = [
...     {0: False, 1: True, 2: True},
...     {0: True, 1: True, 2: False},
... ]

>>> y_pred = [
...     {0: True, 1: True, 2: True},
...     {0: True, 1: False, 2: False},
... ]

>>> jac = metrics.Jaccard()
>>> for yt, yp in zip(y_true, y_pred):
...     jac = jac.update(yt, yp)

>>> jac
Jaccard: 0.583333


## Methods¶

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

get

Return the current value of the metric.

revert

Revert the metric.

Parameters

• y_true (Dict[Union[str, int], Union[bool, str, int]])
• y_pred (Union[Dict[Union[str, int], Union[bool, str, int]], Dict[Union[str, int], Dict[Union[bool, str, int], float]]])
• sample_weight (numbers.Number) – defaults to 1.0
• correction – defaults to None
update

Update the metric.

Parameters

• y_true (Dict[Union[str, int], Union[bool, str, int]])
• y_pred (Union[Dict[Union[str, int], Union[bool, str, int]], Dict[Union[str, int], Dict[Union[bool, str, int], float]]])
• sample_weight (numbers.Number) – defaults to 1.0
works_with

Indicates whether or not a metric can work with a given model.

Parameters

• model (river.base.estimator.Estimator)