scikit-learn · thomasjpfan · Aug 29, 2020 · Aug 26, 2020 · Aug 26, 2020 · Aug 26, 2020
diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css
@@ -839,10 +839,6 @@ div.highlight:hover span.copybutton:hover {
     background-color: #20252B;
 }
 
-div.body img.align-center {
-  max-width: 800px;
-}
-
 div.body img {
     max-width: 100%;
     height: unset!important; /* Needed because sphinx sets the height */
@@ -1234,6 +1230,10 @@ table.sk-sponsor-table td {
   padding: 0.30rem;
 }
 
+.caption {
+  text-align: center
+}
+
 /* pygments - highlightning */
 
 .highlight .hll { background-color: #ffffcc }

diff --git a/doc/tutorial/statistical_inference/finding_help.rst b/doc/tutorial/statistical_inference/finding_help.rst
diff --git a/doc/tutorial/statistical_inference/index.rst b/doc/tutorial/statistical_inference/index.rst
@@ -34,4 +34,3 @@ A tutorial on statistical-learning for scientific data processing
    model_selection
    unsupervised_learning
    putting_together
-   finding_help
diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst
@@ -180,23 +180,20 @@ scoring method.
 .. currentmodule:: sklearn.svm
 
 .. topic:: **Exercise**
-   :class: green
 
-   On the digits dataset, plot the cross-validation score of a :class:`SVC`
-   estimator with an linear kernel as a function of parameter ``C`` (use a
-   logarithmic grid of points, from 1 to 10).
+    On the digits dataset, plot the cross-validation score of a :class:`SVC`
+    estimator with an linear kernel as a function of parameter ``C`` (use a
+    logarithmic grid of points, from 1 to 10).
 
-   .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
-       :lines: 13-23
-    
-   .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
+        .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
+            :lines: 13-23
+
+    .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
         :target: ../../auto_examples/exercises/plot_cv_digits.html
         :align: center
         :scale: 90
 
-   **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`
-
-
+    **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`
 
 Grid-search and cross-validated estimators
 ============================================
@@ -272,7 +269,6 @@ These estimators are called similarly to their counterparts, with 'CV'
 appended to their name.
 
 .. topic:: **Exercise**
-   :class: green
 
    On the diabetes dataset, find the optimal regularization parameter
    alpha.

diff --git a/doc/tutorial/statistical_inference/putting_together.rst b/doc/tutorial/statistical_inference/putting_together.rst
@@ -11,16 +11,13 @@ Pipelining
 We have seen that some estimators can transform data and that some estimators
 can predict variables. We can also create combined estimators:
 
-.. image:: ../../auto_examples/compose/images/sphx_glr_plot_digits_pipe_001.png
-   :target: ../../auto_examples/compose/plot_digits_pipe.html
-   :scale: 65
-   :align: right
-
 .. literalinclude:: ../../auto_examples/compose/plot_digits_pipe.py
     :lines: 23-63
 
-
-
+.. image:: ../../auto_examples/compose/images/sphx_glr_plot_digits_pipe_001.png
+   :target: ../../auto_examples/compose/plot_digits_pipe.html
+   :scale: 65
+   :align: center
 
 Face recognition with eigenfaces
 =================================
@@ -34,26 +31,15 @@ The dataset used in this example is a preprocessed excerpt of the
 
 .. literalinclude:: ../../auto_examples/applications/plot_face_recognition.py
 
-.. |prediction| image:: ../../images/plot_face_recognition_1.png
-   :scale: 50
-
-.. |eigenfaces| image:: ../../images/plot_face_recognition_2.png
+.. figure:: ../../images/plot_face_recognition_1.png
    :scale: 50
 
-.. list-table::
-   :class: centered
-
-   *
+   **Prediction**
 
-     - |prediction|
-
-     - |eigenfaces|
-
-   *
-
-     - **Prediction**
+.. figure:: ../../images/plot_face_recognition_2.png
+   :scale: 50
 
-     - **Eigenfaces**
+   **Eigenfaces**
 
 Expected results for the top 5 most represented people in the dataset::
 

diff --git a/doc/tutorial/statistical_inference/settings.rst b/doc/tutorial/statistical_inference/settings.rst
@@ -38,19 +38,20 @@ needs to be preprocessed in order to be used by scikit-learn.
         >>> digits.images.shape
         (1797, 8, 8)
         >>> import matplotlib.pyplot as plt #doctest: +SKIP
-        >>> plt.imshow(digits.images[-1], cmap=plt.cm.gray_r) #doctest: +SKIP
+        >>> plt.imshow(digits.images[-1],
+        ...            cmap=plt.cm.gray_r) #doctest: +SKIP
         <matplotlib.image.AxesImage object at ...>
 
     .. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
         :target: ../../auto_examples/datasets/plot_digits_last_image.html
-        :align: left
-        :scale: 60
-
+        :align: center
+
     To use this dataset with scikit-learn, we transform each 8x8 image into a
     feature vector of length 64 ::
 
-        >>> data = digits.images.reshape((digits.images.shape[0], -1))
-
+        >>> data = digits.images.reshape(
+        ...     (digits.images.shape[0], -1)
+        ... )
 
 Estimators objects
 ===================