sklearn.metrics.coverage_error¶
-
sklearn.metrics.coverage_error(y_true, y_score, *, sample_weight=None)[source]¶ Coverage error measure.
Compute how far we need to go through the ranked scores to cover all true labels. The best value is equal to the average number of labels in
y_trueper sample.Ties in
y_scoresare broken by giving maximal rank that would have been assigned to all tied values.Note: Our implementation’s score is 1 greater than the one given in Tsoumakas et al., 2010. This extends it to handle the degenerate case in which an instance has 0 true labels.
Read more in the User Guide.
- Parameters
- y_truendarray of shape (n_samples, n_labels)
True binary labels in binary indicator format.
- y_scorendarray of shape (n_samples, n_labels)
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- coverage_errorfloat
References
- 1
Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.