# 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¶

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 (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)