# ExactMatch¶

Exact match score.

This is the most strict multi-label metric, defined as the number of samples that have all their labels correctly classified, divided by the total number of samples.

## Attributes¶

• bigger_is_better

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

• requires_labels

• 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},
...     {0: True, 1: True, 2: False},
... ]

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

>>> metric = metrics.multioutput.ExactMatch()
>>> for yt, yp in zip(y_true, y_pred):
...     metric = metric.update(yt, yp)

>>> metric
ExactMatch: 33.33%


## Methods¶

get

Return the current value of the metric.

is_better_than
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 – defaults to 1.0
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 – defaults to 1.0
works_with

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

Parameters

• model