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FBeta

Binary F-Beta score.

The FBeta score is a weighted harmonic mean between precision and recall. The higher the beta value, the higher the recall will be taken into account. When beta equals 1, precision and recall and equivalently weighted, which results in the F1 score (see metrics.F1).

Parameters

  • beta (float)

    Weight of precision in the harmonic mean.

  • cm – 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.

  • pos_val – defaults to True

    Value to treat as "positive".

Attributes

  • precision (metrics.Precision)

  • recall (metrics.Recall)

Examples

>>> from river import metrics

>>> y_true = [False, False, False, True, True, True]
>>> y_pred = [False, False, True, True, False, False]

>>> metric = metrics.FBeta(beta=2)
>>> for yt, yp in zip(y_true, y_pred):
...     metric = metric.update(yt, yp)

>>> metric
FBeta: 35.71%

Methods

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

  • y_true (bool)
  • y_pred (Union[bool, float, Dict[bool, float]])
  • sample_weight – defaults to 1.0
update

Update the metric.

Parameters

  • y_true (bool)
  • y_pred (Union[bool, float, Dict[bool, float]])
  • sample_weight – defaults to 1.0
works_with

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

Parameters

  • model (river.base.estimator.Estimator)