Take into account amueller comments

examples/tree/plot_structure.py

@@ -6,13 +6,13 @@
 The decision tree structure can be analysed to gain further insight on the
 relation between the features and the target to predict. In this example, we
 show how to retrieve:
-    - the binary tree structure;
-    - the depth of each node and whether or not it's a leaf.
-    - the nodes that were reached by a sample using the ``decision_path``
-      method;
-    - the leaf that was reached by a sample using the apply method;
-    - the rules that were used to predict a sample;
-    - the decision path shared by a group of samples.
+
+- the binary tree structure;
+- the depth of each node and whether or not it's a leaf;
+- the nodes that were reached by a sample using the ``decision_path`` method;
+- the leaf that was reached by a sample using the apply method;
+- the rules that were used to predict a sample;
+- the decision path shared by a group of samples.
 
 """
 import numpy as np
@@ -129,7 +129,7 @@
 
 common_node_id = np.arange(n_nodes)[common_nodes]
 
-print("\nThe following sample %s shares the following node %s  in the tree"
+print("\nThe following sample %s share the node %s  in the tree"
       % (sample_ids, common_node_id))
 print("It is %s %% of all nodes."
       % (len(common_node_id) / n_nodes * 100,))