scikit-learn · adrinjalali · Jun 5, 2024 · May 24, 2024 · May 24, 2024 · May 25, 2024
diff --git a/doc/conf.py b/doc/conf.py
@@ -448,6 +448,7 @@ def add_js_css_files(app, pagename, templatename, context, doctree):
     "auto_examples/exercises/plot_cv_digits.py": (
         "auto_examples/model_selection/plot_nested_cross_validation_iris.py"
     ),
+    "tutorial/machine_learning_map/index.html": "machine_learning_map/index.html",
 }
 html_context["redirects"] = redirects
 for old_link in redirects:

diff --git a/doc/conftest.py b/doc/conftest.py
@@ -128,10 +128,6 @@ def pytest_runtest_setup(item):
         setup_rcv1()
     elif fname.endswith("datasets/twenty_newsgroups.rst") or is_index:
         setup_twenty_newsgroups()
-    elif (
-        fname.endswith("tutorial/text_analytics/working_with_text_data.rst") or is_index
-    ):
-        setup_working_with_text_data()
     elif fname.endswith("modules/compose.rst") or is_index:
         setup_compose()
     elif fname.endswith("datasets/loading_other_datasets.rst"):
@@ -148,8 +144,6 @@ def pytest_runtest_setup(item):
     rst_files_requiring_matplotlib = [
         "modules/partial_dependence.rst",
         "modules/tree.rst",
-        "tutorial/statistical_inference/settings.rst",
-        "tutorial/statistical_inference/supervised_learning.rst",
     ]
     for each in rst_files_requiring_matplotlib:
         if fname.endswith(each):

diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
@@ -651,10 +651,8 @@ We are glad to accept any sort of documentation:
 * **User guide:** These provide more detailed information about the algorithms
   implemented in scikit-learn and generally live in the root
   `doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_ directory
-  and `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
-* **Tutorials:** These introduce various statistical learning and machine learning
-  concepts and are located in
-  `doc/tutorial <https://github.com/scikit-learn/scikit-learn/tree/main/doc/tutorial>`_.
+  and
+  `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
 * **Examples:** These provide full code examples that may demonstrate the use
   of scikit-learn modules, compare different algorithms or discuss their
   interpretation, etc. Examples live in

diff --git a/doc/getting_started.rst b/doc/getting_started.rst
@@ -53,6 +53,8 @@ new data. You don't need to re-train the estimator::
   >>> clf.predict([[4, 5, 6], [14, 15, 16]])  # predict classes of new data
   array([0, 1])
 
+You can check :ref:`ml_map` on how to choose the right model for your use case.
+
 Transformers and pre-processors
 -------------------------------
 
@@ -227,6 +229,3 @@ provide. You can also find an exhaustive list of the public API in the
 
 You can also look at our numerous :ref:`examples <general_examples>` that
 illustrate the use of ``scikit-learn`` in many different contexts.
-
-The :ref:`tutorials <tutorial_menu>` also contain additional learning
-resources.
diff --git a/doc/tutorial/machine_learning_map/README.md → doc/images/ml_map.README.rst b/doc/tutorial/machine_learning_map/README.md → doc/images/ml_map.README.rst
diff --git a/doc/index.rst.template b/doc/index.rst.template
@@ -13,7 +13,6 @@
    auto_examples/index
    Community <https://blog.scikit-learn.org/>
    getting_started
-   Tutorials <tutorial/index>
    whats_new
    Glossary <glossary>
    Development <{{ development_link }}>

diff --git a/doc/tutorial/machine_learning_map/index.rst → doc/machine_learning_map.rst b/doc/tutorial/machine_learning_map/index.rst → doc/machine_learning_map.rst
@@ -14,7 +14,7 @@ problems with regard to which estimators to try on your data. Click on any estim
 the chart below to see its documentation. The 😭 emoji is to be read as "if this
 estimator does not achieve the desired outcome, then follow the arrow and try the next
 one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can
-also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
+also download the chart: :download:`ml_map.svg <images/ml_map.svg>`.
 
 .. raw:: html
 
@@ -36,7 +36,7 @@ also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
     }
   </style>
 
-  <script src="../../_static/scripts/vendor/svg-pan-zoom.min.js"></script>
+  <script src="_static/scripts/vendor/svg-pan-zoom.min.js"></script>
   <script>
     document.addEventListener("DOMContentLoaded", function () {
       const beforePan = function (oldPan, newPan) {
@@ -69,7 +69,7 @@ also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
   <div id="sk-ml-map">
 
 .. raw:: html
-  :file: ../../images/ml_map.svg
+  :file: images/ml_map.svg
 
 .. raw:: html
 

diff --git a/doc/presentations.rst b/doc/presentations.rst
@@ -2,9 +2,6 @@
 External Resources, Videos and Talks
 ===========================================
 
-For written tutorials, see the :ref:`Tutorial section <tutorial_menu>` of
-the documentation.
-
 New to Scientific Python?
 ==========================
 For those that are still new to the scientific Python ecosystem, we highly
@@ -45,9 +42,7 @@ Videos
 
   An extensive tutorial, consisting of four sessions of one hour.
   The tutorial covers the basics of machine learning,
-  many algorithms and how to apply them using scikit-learn. The
-  material corresponding is now in the scikit-learn documentation
-  section :ref:`stat_learn_tut_index`.
+  many algorithms and how to apply them using scikit-learn.
 
 - `Statistical Learning for Text Classification with scikit-learn and NLTK
   <https://pyvideo.org/video/417/pycon-2011--statistical-machine-learning-for-text>`_