DOC Fix various sphinx warnings. (#15692)

cmarmo · jnothman · commit 46a6b8b8939c · 2019-11-28T20:51:43.000+11:00
diff --git a/doc/faq.rst b/doc/faq.rst
@@ -392,11 +392,11 @@ Currently transform only works for features X in a pipeline.
 There's a long-standing discussion about 
 not being able to transform y in a pipeline.
 Follow on github issue
-`#4143<https://github.com/scikit-learn/scikit-learn/issues/4143>`_.
+`#4143 <https://github.com/scikit-learn/scikit-learn/issues/4143>`_.
 Meanwhile check out
 :class:`sklearn.compose.TransformedTargetRegressor`,
-`pipegraph<https://github.com/mcasl/PipeGraph>`_,
-`imbalanced-learn<https://github.com/scikit-learn-contrib/imbalanced-learn>`_.
+`pipegraph <https://github.com/mcasl/PipeGraph>`_,
+`imbalanced-learn <https://github.com/scikit-learn-contrib/imbalanced-learn>`_.
 Note that Scikit-learn solved for the case where y 
 has an invertible transformation applied before training 
 and inverted after prediction. Scikit-learn intends to solve for
diff --git a/examples/gaussian_process/plot_gpr_on_structured_data.py b/examples/gaussian_process/plot_gpr_on_structured_data.py
@@ -33,8 +33,8 @@
 four correct classifications and fails on one.
 
 .. [1] Haussler, D. (1999). Convolution kernels on discrete structures
-(Vol. 646). Technical report, Department of Computer Science, University of
-California at Santa Cruz.
+       (Vol. 646). Technical report, Department of Computer Science, University
+       of California at Santa Cruz.
 """
 print(__doc__)
 
diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
@@ -1563,6 +1563,7 @@ class ExtraTreesClassifier(ForestClassifier):
         - the sampling of the features to consider when looking for the best
           split at each node (if ``max_features < n_features``)
         - the draw of the splits for each of the `max_features`
+
         See :term:`Glossary <random_state>` for details.
 
     verbose : int, optional (default=0)
@@ -1871,6 +1872,7 @@ class ExtraTreesRegressor(ForestRegressor):
         - the sampling of the features to consider when looking for the best
           split at each node (if ``max_features < n_features``)
         - the draw of the splits for each of the `max_features`
+
         See :term:`Glossary <random_state>` for details.
 
     verbose : int, optional (default=0)
