MultiOutputClassificationMetric

Mother class for all multi-output classification metrics.

Parameters

• cm (river.metrics.multioutput.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

• works_with_weights

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

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.

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 (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)