GeometricMean

Geometric mean score.

The geometric mean is a good indicator of a classifier's performance in the presence of class imbalance because it is independent of the distribution of examples between classes. This implementation computes the geometric mean of class-wise sensitivity (recall).

$$gm=\sqrt[n]{s_1\cdot s_2\cdot s_3\cdot \dots \cdot s_n}$$

where $s_i$ is the sensitivity (recall) of class $i$ and $n$ is the number of classes.

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 = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird', 'bird']
>>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat', 'bird']

>>> metric = metrics.GeometricMean()

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

>>> metric
GeometricMean: 69.34%

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

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1. Barandela, R. et al. “Strategies for learning in class imbalance problems”, Pattern Recognition, 36(3), (2003), pp 849-851. ↩