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Ndcg

The one to report. Each row's discounted gain is divided by the gain of its own perfect ranking, so the result is in [0, 1] regardless of how relevance was scaled and rows with different judgement scales can be averaged together.

The ideal is computed without tie averaging, as scikit-learn computes it: ranking a row by its own relevance leaves ties only between equal gains, which no ordering can separate. A row where nothing is relevant has no ideal to divide by and scores 0.

No logBase, unlike Dcg, because ndcg_score has none — the discount cancels in the ratio when both halves share a base, and scikit-learn shares base 2.

Members

Member	What it does
Ndcg.Score	The mean normalized discounted gain over the rows, in [0, 1].