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[Doc] Document that AUC and AUCPR are for binary classification/ranking [skip ci] (#5899)

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Philip Hyunsu Cho 2020-12-06 22:17:20 -08:00 committed by GitHub
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doc/parameter.rst
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@ -399,8 +399,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``error@t``: a different than 0.5 binary classification threshold value could be specified by providing a numerical value through 't'.
- ``merror``: Multiclass classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``.
- ``mlogloss``: `Multiclass logloss <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html>`_.
- ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
- ``aucpr``: `Area under the PR curve <https://en.wikipedia.org/wiki/Precision_and_recall>`_
- ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_. Available for binary classification and learning-to-rank tasks.
- ``aucpr``: `Area under the PR curve <https://en.wikipedia.org/wiki/Precision_and_recall>`_. Available for binary classification and learning-to-rank tasks.
- ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
- ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
- ``ndcg@n``, ``map@n``: 'n' can be assigned as an integer to cut off the top positions in the lists for evaluation.