Merge pull request scikit-learn#2163 from ianozsvald/fix_plot_forest_iris_docs

Updated docs to fix formatting errors

Author: NelleV

examples/ensemble/plot_forest_iris.py

@@ -20,21 +20,23 @@
     ExtraTreesClassifier()  # 0.95 score
     RandomForestClassifier()  # 0.94 score
     AdaBoost(DecisionTree(max_depth=3))  # 0.94 score
-    DecisionTree(max_depth=None)  # 0.94 score``
+    DecisionTree(max_depth=None)  # 0.94 score
 
 Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
 the average score does not improve).
 
 See the console's output for further details about each model.
 
 In this example you might try to:
-1) vary the `max_depth` for the DecisionTreeClassifier and AdaBoostClassifier, perhaps
-   try ``max_depth=3`` for the DecisionTreeClassifier or ``max_depth=None``
-   for AdaBoostClassifier
-2) vary `n_estimators`
 
-Remember that RandomForests and ExtraTrees can be fitted in parallel (each tree is
-built independently of the others), AdaBoost's samples are built iteratively.
+1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
+   ``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
+   ``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
+2) vary ``n_estimators``
+
+It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
+on many cores as each tree is built independently of the others. AdaBoost's
+samples are built sequentially and so do not use multiple cores.
 """
 print(__doc__)