sklearn.metrics.coverage_error(y_true, y_score, sample_weight=None)
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_true per sample.
y_scores are 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.
|||Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.|
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