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[doc] Fix broken links. (#7341)

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* Fix most of the link checks from sphinx.
* Remove duplicate explicit target name.
This commit is contained in:
Jiaming Yuan
2021-10-20 14:45:30 +08:00
committed by GitHub
parent f53da412aa
commit 376b448015
7 changed files with 24 additions and 17 deletions
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doc/parameter.rst
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@@ -366,8 +366,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
- ``rank:ndcg``: Use LambdaMART to perform list-wise ranking where `Normalized Discounted Cumulative Gain (NDCG) <http://en.wikipedia.org/wiki/NDCG>`_ is maximized
- ``rank:map``: Use LambdaMART to perform list-wise ranking where `Mean Average Precision (MAP) <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_ is maximized
- ``reg:gamma``: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Applications>`_.
- ``reg:tweedie``: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Applications>`_.
- ``reg:gamma``: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Occurrence_and_applications>`_.
- ``reg:tweedie``: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Occurrence_and_applications>`_.
* ``base_score`` [default=0.5]
@@ -390,7 +390,7 @@ 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``: `Receiver Operating Characteristic Area under the Curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_.
- ``auc``: `Receiver Operating Characteristic Area under the Curve <https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve>`_.
Available for classification and learning-to-rank tasks.
- When used with binary classification, the objective should be ``binary:logistic`` or similar functions that work on probability.