# MultiFBeta¶

Multi-class F-Beta score with different betas per class.

The multiclass F-Beta score is the arithmetic average of the binary F-Beta scores of each class. The mean can be weighted by providing class weights.

## Parameters¶

• betas

Weight of precision in the harmonic mean of each class.

• weights

Class weights. If not provided then uniform weights will be used.

• 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.

## Attributes¶

• bigger_is_better

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

• requires_labels

Indicates if labels are required, rather than probabilities.

• works_with_weights

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

## Examples¶

>>> from river import metrics

>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]

>>> metric = metrics.MultiFBeta(
...     betas={0: 0.25, 1: 1, 2: 4},
...     weights={0: 1, 1: 1, 2: 2}
... )

>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp))
MultiFBeta: 100.00%
MultiFBeta: 25.76%
MultiFBeta: 62.88%
MultiFBeta: 62.88%
MultiFBeta: 46.88%


## Methods¶

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

• y_true
• y_pred
• sample_weight – defaults to 1.0
update

Update the metric.

Parameters

• y_true
• y_pred
• 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)