cohen_kappa_score#
- sklearn.metrics.cohen_kappa_score(y1, y2, *, labels=None, weights=None, sample_weight=None)[source]#
Compute Cohen’s kappa: a statistic that measures inter-annotator agreement.
This function computes Cohen’s kappa [1], a score that expresses the level of agreement between two annotators on a classification problem. It is defined as
where
is the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and is the expected agreement when both annotators assign labels randomly. is estimated using a per-annotator empirical prior over the class labels [2].Read more in the User Guide.
- Parameters:
- y1array-like of shape (n_samples,)
Labels assigned by the first annotator.
- y2array-like of shape (n_samples,)
Labels assigned by the second annotator. The kappa statistic is symmetric, so swapping
y1
andy2
doesn’t change the value.- labelsarray-like of shape (n_classes,), default=None
List of labels to index the matrix. This may be used to select a subset of labels. If
None
, all labels that appear at least once iny1
ory2
are used.- weights{‘linear’, ‘quadratic’}, default=None
Weighting type to calculate the score.
None
means no weighted; “linear” means linear weighted; “quadratic” means quadratic weighted.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- kappafloat
The kappa statistic, which is a number between -1 and 1. The maximum value means complete agreement; zero or lower means chance agreement.
References
Examples
>>> from sklearn.metrics import cohen_kappa_score >>> y1 = ["negative", "positive", "negative", "neutral", "positive"] >>> y2 = ["negative", "positive", "negative", "neutral", "negative"] >>> cohen_kappa_score(y1, y2) np.float64(0.6875)