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

Indicate if the current metric is better than another one.

Parameters

• other

revert

Revert the metric.

Parameters

• y_true — 'bool'
• y_pred — 'bool | float | dict[bool, float]'
• sample_weight — defaults to 1.0

update

Update the metric.

Parameters

• y_true — 'bool'
• y_pred — '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