docs: fix borken links after openml.org rework

doc/index.rst

@@ -71,7 +71,7 @@ Further information
 
 * `OpenML documentation <https://docs.openml.org/>`_
 * `OpenML client APIs <https://docs.openml.org/APIs/>`_
-* `OpenML developer guide <https://docs.openml.org/Contributing/>`_
+* `OpenML developer guide <https://docs.openml.org/contributing/Contributing/>`_
 * `Contact information <https://www.openml.org/contact>`_
 * `Citation request <https://www.openml.org/cite>`_
 * `OpenML blog <https://medium.com/open-machine-learning>`_

doc/usage.rst

@@ -118,7 +118,7 @@ this should be repeated several times. Also, the task defines a target metric
 for which a flow should be optimized.
 
 Below you can find our tutorial regarding tasks and if you want to know more
-you can read the `OpenML guide <https://docs.openml.org/#tasks>`_:
+you can read the `OpenML guide <https://docs.openml.org/concepts/tasks/>`_:
 
 * :ref:`sphx_glr_examples_30_extended_tasks_tutorial.py`
 

examples/40_paper/2015_neurips_feurer_example.py

@@ -49,14 +49,14 @@
 #    this does not allow reproducibility (unclear splitting). Please do not use datasets but the
 #    respective tasks as basis for a paper and publish task IDS. This example is only given to
 #    showcase the use of OpenML-Python for a published paper and as a warning on how not to do it.
-#    Please check the `OpenML documentation of tasks <https://docs.openml.org/#tasks>`_ if you
+#    Please check the `OpenML documentation of tasks <https://docs.openml.org/concepts/tasks/>`_ if you
 #    want to learn more about them.
 
 ####################################################################################################
 # This lists both active and inactive tasks (because of ``status='all'``). Unfortunately,
 # this is necessary as some of the datasets contain issues found after the publication and became
 # deactivated, which also deactivated the tasks on them. More information on active or inactive
-# datasets can be found in the `online docs <https://docs.openml.org/#dataset-status>`_.
+# datasets can be found in the `online docs <https://docs.openml.org/concepts/data/#dataset-status>`_.
 tasks = openml.tasks.list_tasks(
     task_type=openml.tasks.TaskType.SUPERVISED_CLASSIFICATION,
     status="all",

openml/datasets/functions.py

@@ -861,7 +861,7 @@ def status_update(data_id: int, status: Literal["active", "deactivated"]) -> Non
     Updates the status of a dataset to either 'active' or 'deactivated'.
     Please see the OpenML API documentation for a description of the status
     and all legal status transitions:
-    https://docs.openml.org/#dataset-status
+    https://docs.openml.org/concepts/data/#dataset-status
 
     Parameters
     ----------

openml/runs/functions.py

@@ -74,8 +74,7 @@ def run_model_on_task(  # noqa: PLR0913
     ----------
     model : sklearn model
         A model which has a function fit(X,Y) and predict(X),
-        all supervised estimators of scikit learn follow this definition of a model
-        (https://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)
+        all supervised estimators of scikit learn follow this definition of a model.
     task : OpenMLTask or int or str
         Task to perform or Task id.
         This may be a model instead if the first argument is an OpenMLTask.
@@ -199,8 +198,7 @@ def run_flow_on_task(  # noqa: C901, PLR0912, PLR0915, PLR0913
     flow : OpenMLFlow
         A flow wraps a machine learning model together with relevant information.
         The model has a function fit(X,Y) and predict(X),
-        all supervised estimators of scikit learn follow this definition of a model
-        (https://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)
+        all supervised estimators of scikit learn follow this definition of a model.
     task : OpenMLTask
         Task to perform. This may be an OpenMLFlow instead if the first argument is an OpenMLTask.
     avoid_duplicate_runs : bool, optional (default=True)