[docs] fix broken links (#6161)

docs/.linkcheckerrc

@@ -11,7 +11,7 @@ ignore=
   http.*amd.com/.*
   https.*dl.acm.org/doi/.*
   https.*tandfonline.com/.*
-ignorewarnings=http-robots-denied,https-certificate-error
+ignorewarnings=http-redirected,http-robots-denied,https-certificate-error
 checkextern=1
 
 [output]

docs/Experiments.rst

@@ -25,7 +25,7 @@ We used 5 datasets to conduct our comparison experiments. Details of data are li
 +-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+
 | 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 <http://research.microsoft.com/en-us/projects/mslr/>`__          | 2,270,296   | 137      | {S1,S2,S3} as train set, {S5} as test set    |
+| 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 |
 +-----------+-----------------------+------------------------------------------------------------------------+-------------+----------+----------------------------------------------+

docs/FAQ.rst

@@ -289,7 +289,7 @@ Python-package
 
 This error should be solved in latest version.
 If you still meet this error, try to remove ``lightgbm.egg-info`` folder in your Python-package and reinstall,
-or check `this thread on stackoverflow <http://stackoverflow.com/questions/18085571/pip-install-error-setup-script-specifies-an-absolute-path>`__.
+or check `this thread on stackoverflow <https://stackoverflow.com/questions/18085571/pip-install-error-setup-script-specifies-an-absolute-path>`__.
 
 2. Error messages: ``Cannot ... before construct dataset``.
 -----------------------------------------------------------

docs/GPU-Performance.rst

@@ -196,7 +196,7 @@ Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boos
 
 .. _link1: https://archive.ics.uci.edu/ml/datasets/HIGGS
 
-.. _link2: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
+.. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
 
 .. _link3: https://www.kaggle.com/c/bosch-production-line-performance/data
 

docs/Installation-Guide.rst

@@ -960,7 +960,7 @@ gcc
 
 .. _Boost Binaries: https://sourceforge.net/projects/boost/files/boost-binaries/
 
-.. _SWIG: http://www.swig.org/download.html
+.. _SWIG: https://www.swig.org/download.html
 
 .. _this detailed guide: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
 

docs/Parallel-Learning-Guide.rst

@@ -384,8 +384,6 @@ From the point forward, you can use any of the following methods to save the Boo
 Kubeflow
 ^^^^^^^^
 
-`Kubeflow Fairing`_ supports LightGBM distributed training. `These examples`_ show how to get started with LightGBM and Kubeflow Fairing in a hybrid cloud environment.
-
 Kubeflow users can also use the `Kubeflow XGBoost Operator`_ for machine learning workflows with LightGBM. You can see `this example`_ for more details.
 
 Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers.
@@ -528,10 +526,6 @@ See `the mars documentation`_ for usage examples.
 
 .. _these Dask examples: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask
 
-.. _Kubeflow Fairing: https://www.kubeflow.org/docs/components/fairing/fairing-overview
-
-.. _These examples: https://github.com/kubeflow/fairing/tree/master/examples/lightgbm
-
 .. _Kubeflow XGBoost Operator: https://github.com/kubeflow/xgboost-operator
 
 .. _this example: https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/lightgbm-dist

docs/gcc-Tips.rst

@@ -25,8 +25,6 @@ You can find more details on the experimentation below:
 
 -  `Laurae's Benchmark Master Data (Interactive) <https://public.tableau.com/views/gbt_benchmarks/Master-Data?:showVizHome=no>`__
 
--  `Kaggle Paris Meetup #12 Slides <https://drive.google.com/file/d/0B6qJBmoIxFe0ZHNCOXdoRWMxUm8/view>`__
-
 The image below compares the runtime for training with different compiler options to a baseline using LightGBM compiled with ``-O2 --mtune=core2``. All three options are faster than that baseline. The best performance was achieved with ``-O3 --mtune=native``.
 
 .. image:: ./_static/images/gcc-comparison-2.png