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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

    Type → float

    Weight of precision in the harmonic mean.

  • cm

    Default → 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.update(yt, yp)

metric

MicroFBeta: 60.00%

Methods

get

Return the current value of the metric.

is_better_than

Indicate if the current metric is better than another one.

Parameters

  • other

revert

Revert the metric.

Parameters

  • y_true
  • y_pred
  • w — defaults to 1.0

update

Update the metric.

Parameters

  • y_true
  • y_pred
  • w — defaults to 1.0

works_with

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

Parameters

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