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LogLoss

Binary logarithmic loss.

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, False, False, True]
>>> y_pred = [0.9,  0.1,   0.2,   0.65]

>>> metric = metrics.LogLoss()
>>> for yt, yp in zip(y_true, y_pred):
...     metric = metric.update(yt, yp)
...     print(metric.get())
0.105360
0.105360
0.144621
0.216161

>>> metric
LogLoss: 0.216162

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