[docs] reduce redirects in docs links (#6181)

docs/Experiments.rst

@@ -18,19 +18,19 @@ Data
 
 We used 5 datasets to conduct our comparison experiments. Details of data are listed in the following table:
 
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
-| Data      | Task                  | Link                                                                   | #Train\_Set | #Feature | Comments                                     |
-+===========+=======================+========================================================================+=============+==========+==============================================+
-| Higgs     | Binary classification | `link <https://archive.ics.uci.edu/ml/datasets/HIGGS>`__               | 10,500,000  | 28       | last 500,000 samples were used as test set   |
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
-| Yahoo LTR | Learning to rank      | `link <https://webscope.sandbox.yahoo.com/catalog.php?datatype=c>`__   | 473,134     | 700      | set1.train as train, set1.test as test       |
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
-| MS LTR    | Learning to rank      | `link <https://www.microsoft.com/en-us/research/project/mslr/>`__      | 2,270,296   | 137      | {S1,S2,S3} as train set, {S5} as test set    |
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
-| Expo      | Binary classification | `link <http://stat-computing.org/dataexpo/2009/>`__                    | 11,000,000  | 700      | last 1,000,000 samples were used as test set |
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
-| Allstate  | Binary classification | `link <https://www.kaggle.com/c/ClaimPredictionChallenge>`__           | 13,184,290  | 4228     | last 1,000,000 samples were used as test set |
-+-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
+| Data      | Task                  | Link                                                                            | #Train\_Set | #Feature | Comments                                     |
++===========+=======================+=================================================================================+=============+==========+==============================================+
+| Higgs     | Binary classification | `link <https://archive.ics.uci.edu/dataset/280/higgs>`__                        | 10,500,000  | 28       | last 500,000 samples were used as test set   |
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
+| Yahoo LTR | Learning to rank      | `link <https://webscope.sandbox.yahoo.com/catalog.php?datatype=c>`__            | 473,134     | 700      | set1.train as train, set1.test as test       |
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
+| MS LTR    | Learning to rank      | `link <https://www.microsoft.com/en-us/research/project/mslr/>`__               | 2,270,296   | 137      | {S1,S2,S3} as train set, {S5} as test set    |
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
+| Expo      | Binary classification | `link <https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009>`__ | 11,000,000  | 700      | last 1,000,000 samples were used as test set |
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
+| Allstate  | Binary classification | `link <https://www.kaggle.com/c/ClaimPredictionChallenge>`__                    | 13,184,290  | 4228     | last 1,000,000 samples were used as test set |
++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
 
 Environment
 ^^^^^^^^^^^

docs/Features.rst

@@ -291,7 +291,7 @@ References
 
 .. _On Grouping for Maximum Homogeneity: https://www.tandfonline.com/doi/abs/10.1080/01621459.1958.10501479
 
-.. _Optimization of collective communication operations in MPICH: https://www.mcs.anl.gov/~thakur/papers/ijhpca-coll.pdf
+.. _Optimization of collective communication operations in MPICH: https://web.cels.anl.gov/~thakur/papers/ijhpca-coll.pdf
 
 .. _A Communication-Efficient Parallel Algorithm for Decision Tree: http://papers.nips.cc/paper/6381-a-communication-efficient-parallel-algorithm-for-decision-tree
 

docs/GPU-Performance.rst

@@ -194,17 +194,17 @@ following article:
 
 Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018.
 
-.. _link1: https://archive.ics.uci.edu/ml/datasets/HIGGS
+.. _link1: https://archive.ics.uci.edu/dataset/280/higgs
 
 .. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
 
 .. _link3: https://www.kaggle.com/c/bosch-production-line-performance/data
 
 .. _link4: https://webscope.sandbox.yahoo.com/catalog.php?datatype=c
 
-.. _link5: http://research.microsoft.com/en-us/projects/mslr/
+.. _link5: https://www.microsoft.com/en-us/research/project/mslr/
 
-.. _link6: http://stat-computing.org/dataexpo/2009/
+.. _link6: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009
 
 .. _0bb4a82: https://github.com/microsoft/LightGBM/commit/0bb4a82
 

docs/Installation-Guide.rst

@@ -950,7 +950,7 @@ gcc
 
 .. _RDMA: https://en.wikipedia.org/wiki/Remote_direct_memory_access
 
-.. _MS MPI: https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes
+.. _MS MPI: https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes
 
 .. _Open MPI: https://www.open-mpi.org/
 

docs/Parallel-Learning-Guide.rst

@@ -518,7 +518,7 @@ See `the mars documentation`_ for usage examples.
 
 .. _the Dask DataFrame documentation: https://docs.dask.org/en/latest/dataframe.html
 
-.. _the Dask prediction example: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask/prediction.py
+.. _the Dask prediction example: https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/dask/prediction.py
 
 .. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html
 
@@ -536,7 +536,7 @@ See `the mars documentation`_ for usage examples.
 
 .. _lightgbm_ray: https://github.com/ray-project/lightgbm_ray
 
-.. _Ray: https://ray.io/
+.. _Ray: https://www.ray.io/
 
 .. _the lightgbm_ray documentation: https://docs.ray.io/en/latest/tune/api_docs/integration.html#lightgbm-tune-integration-lightgbm
 

docs/Parameters.rst

@@ -119,7 +119,7 @@ Core Parameters
 
    -  ranking application
 
-      -  ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
+      -  ``lambdarank``, `lambdarank <https://proceedings.neurips.cc/paper_files/paper/2006/file/af44c4c56f385c43f2529f9b1b018f6a-Paper.pdf>`__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``
 
       -  ``rank_xendcg``, `XE_NDCG_MART <https://arxiv.org/abs/1911.09798>`__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``
 
@@ -536,15 +536,15 @@ Learning Control Parameters
 
       -  ``basic``, the most basic monotone constraints method. It does not slow the library at all, but over-constrains the predictions
 
-      -  ``intermediate``, a `more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
+      -  ``intermediate``, a `more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library very slightly. However, this method is much less constraining than the basic method and should significantly improve the results
 
-      -  ``advanced``, an `even more advanced method <https://hal.archives-ouvertes.fr/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
+      -  ``advanced``, an `even more advanced method <https://hal.science/hal-02862802/document>`__, which may slow the library. However, this method is even less constraining than the intermediate method and should again significantly improve the results
 
 -  ``monotone_penalty`` :raw-html:`<a id="monotone_penalty" title="Permalink to this parameter" href="#monotone_penalty">&#x1F517;&#xFE0E;</a>`, default = ``0.0``, type = double, aliases: ``monotone_splits_penalty``, ``ms_penalty``, ``mc_penalty``, constraints: ``monotone_penalty >= 0.0``
 
    -  used only if ``monotone_constraints`` is set
 
-   -  `monotone penalty <https://hal.archives-ouvertes.fr/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
+   -  `monotone penalty <https://hal.science/hal-02862802/document>`__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter
 
    -  if ``0.0`` (the default), no penalization is applied
 
@@ -564,7 +564,7 @@ Learning Control Parameters
 
    -  **Note**: the forced split logic will be ignored, if the split makes gain worse
 
-   -  see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
+   -  see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/binary_classification/forced_splits.json>`__ as an example
 
 -  ``refit_decay_rate`` :raw-html:`<a id="refit_decay_rate" title="Permalink to this parameter" href="#refit_decay_rate">&#x1F517;&#xFE0E;</a>`, default = ``0.9``, type = double, constraints: ``0.0 <= refit_decay_rate <= 1.0``
 
@@ -770,7 +770,7 @@ Dataset Parameters
 
 -  ``enable_bundle`` :raw-html:`<a id="enable_bundle" title="Permalink to this parameter" href="#enable_bundle">&#x1F517;&#xFE0E;</a>`, default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle``
 
-   -  set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`__
+   -  set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html>`__
 
    -  **Note**: disabling this may cause the slow training speed for sparse datasets
 
@@ -894,7 +894,7 @@ Dataset Parameters
 
    -  ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)
 
-   -  see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example
+   -  see `this file <https://github.com/microsoft/LightGBM/blob/master/examples/regression/forced_bins.json>`__ as an example
 
 -  ``save_binary`` :raw-html:`<a id="save_binary" title="Permalink to this parameter" href="#save_binary">&#x1F517;&#xFE0E;</a>`, default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file``
 
@@ -961,7 +961,7 @@ Predict Parameters
 
    -  produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
 
-   -  **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
+   -  **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/shap>`__
 
    -  **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
 

docs/Quick-Start.rst

@@ -85,4 +85,4 @@ Examples
 
 .. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/
 
-.. _Expo data: http://stat-computing.org/dataexpo/2009/
+.. _Expo data: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009