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.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]'
- w — defaults to
1.0
update
Update the metric.
Parameters
- y_true — 'bool'
- y_pred — 'bool | float | dict[bool, float]'
- w — defaults to
1.0
works_with
Indicates whether or not a metric can work with a given model.
Parameters
- model