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F1

Binary F1 score.

Parameters

  • 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.

  • pos_val

    Default → True

    Value to treat as "positive".

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 = [False, False, False, True, True, True]
y_pred = [False, False, True, True, False, False]

metric = metrics.F1()

for yt, yp in zip(y_true, y_pred):
    metric = metric.update(yt, yp)

metric

F1: 40.00%

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 — 'bool'
  • y_pred — 'bool | float | dict[bool, float]'
  • sample_weight — defaults to 1.0

update

Update the metric.

Parameters

  • y_true — 'bool'
  • y_pred — '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