label_ranking_loss#

sklearn.metrics.label_ranking_loss(y_true, y_score, *, sample_weight=None)[source]#

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.

Added 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_scorearray-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_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

lossfloat: 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.

Examples

>>> from sklearn.metrics import label_ranking_loss
>>> y_true = [[1, 0, 0], [0, 0, 1]]
>>> y_score = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
>>> label_ranking_loss(y_true, y_score)
np.float64(0.75...)