MicroFBeta

Micro-average F-Beta score.

This computes the F-Beta score by merging all the predictions and true labels, and then computes a global F-Beta score.

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.

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

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

>>> metric
MicroFBeta: 60.00%

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)

References

1. Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem?