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LabelRankingLoss

How often the ranking got a pair the wrong way round. Every (relevant, irrelevant) pair of labels in a sample is one comparison the model either won or lost, and this is the fraction it lost, averaged over the samples. 0 is perfect and 1 is every relevant label buried under every irrelevant one.

It is the pairwise counterpart of CoverageError: coverage reads down to the single worst-placed relevant label and reports a position, this counts every pair and reports a fraction, so a row with one badly ranked label out of many hurts coverage far more than it hurts this. Reported together, the two say whether a bad number comes from one outlier or from the whole ordering.

A tie counts as an error. An irrelevant label sharing a relevant one's score is counted as outranking it, so a row whose scores are all equal scores 1 rather than 0.5. That is the reference's arithmetic — the rank of a tied group is its worst — and the frozen corpus pins it.

A sample where every label or no label is relevant holds no pair to order and contributes 0. A single label column is refused with scikit-learn's sentence, binary format is not supported, as CoverageError refuses it and LabelRankingAveragePrecision does not.

Members

Member	What it does
LabelRankingLoss.Score	The mean fraction of wrongly ordered label pairs, in [0, 1].