MCC¶

Matthews correlation coefficient.

Parameters¶

cm – 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.
pos_val – defaults to 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 = [True, True, True, False]
>>> y_pred = [True, False, True, True]

>>> mcc = metrics.MCC()

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

>>> mcc
MCC: -0.333333

Methods¶

get

Return the current value of the metric.

is_better_than

revert

Revert the metric.

Parameters

y_true (bool)
y_pred (Union[bool, float, Dict[bool, float]])
sample_weight – defaults to 1.0

update

Update the metric.

Parameters

y_true (bool)
y_pred (Union[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

model (river.base.estimator.Estimator)

References¶

Wikipedia article ↩