MicroRecall¶

Micro-average recall score.

The micro-average recall is exactly equivalent to the micro-average precision as well as the micro-average F1 score.

Parameters¶

• cm (river.metrics.confusion.ConfusionMatrix) – 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.MicroRecall()

>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp))
MicroRecall: 100.00%
MicroRecall: 50.00%
MicroRecall: 66.67%
MicroRecall: 75.00%
MicroRecall: 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)