CohenKappa¶
Cohen's Kappa score.
Cohen's Kappa expresses the level of agreement between two annotators on a classification problem. It is defined as
where \(p_o\) is the empirical probability of agreement on the label assigned to any sample (prequential accuracy), and \(p_e\) is the expected agreement when both annotators assign labels randomly.
Parameters¶
-
cm
Default →
None
This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.
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 = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']
y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']
metric = metrics.CohenKappa()
for yt, yp in zip(y_true, y_pred):
metric.update(yt, yp)
metric
CohenKappa: 42.86%
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
- w — defaults to
1.0
update
Update the metric.
Parameters
- y_true
- y_pred
- w — defaults to
1.0
works_with
Indicates whether or not a metric can work with a given model.
Parameters
- model — 'base.Estimator'
-
J. Cohen (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement 20(1):37-46. doi:10.1177/001316446002000104. ↩