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MicroPrecision

Micro-average precision score.

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

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.MicroPrecision()

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

MicroPrecision: 100.00%
MicroPrecision: 50.00%
MicroPrecision: 66.67%
MicroPrecision: 75.00%
MicroPrecision: 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? ↩