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scikit-learn 1.3.2
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# `sklearn.metrics`.label_ranking_loss

sklearn.metrics.label_ranking_loss(y_true, y_score, *, sample_weight=None)

  Compute Ranking loss measure.

  Compute the average number of label pairs that are incorrectly ordered given y_score weighted by the size of the label set and the number of labels not in the label set.

  This is similar to the error set size, but weighted by the number of relevant and irrelevant labels. The best performance is achieved with a ranking loss of zero.

  Read more in the User Guide.

  New in version 0.17: A function label_ranking_loss

  Parameters:
    y_true{array-like, sparse matrix} of shape (n_samples, n_labels)

      True binary labels in binary indicator format.

    y_score array-like 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_weight array-like of shape (n_samples,), default=None

      Sample weights.

  Returns:
    loss float

      Average number of label pairs that are incorrectly ordered given y_score weighted by the size of the label set and the number of labels not in the label set.

  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.