CrossEntropy

Multiclass generalization of the 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 = [0, 1, 2, 2]
y_pred = [
    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},
    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},
    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},
    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}
]

metric = metrics.CrossEntropy()

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

1.222454
1.169691
1.258864
1.321597

metric

CrossEntropy: 1.321598

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
• 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