added more on trees

doc/src/week44/week44.do.txt

@@ -44,7 +44,8 @@ to be the most informative ones. The process of finding the _most
 informative_ feature is done until we accomplish a stopping criteria
 where we then finally end up in so called _leaf nodes_. 
 
-
+!split
+===== Basics of a tree =====
 
 A decision tree is typically divided into a _root node_, the _interior nodes_,
 and the final _leaf nodes_ or just _leaves_. These entities are then connected by so-called _branches_.
@@ -56,6 +57,18 @@ learned the underlying structure of the training data and hence can,
 given some assumptions, make predictions about the target feature value
 (class) of unseen query instances.
 
+!split
+===== A Sketch of a Tree, Regression problem  =====
+
+#FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8]
+
+!split
+===== A Sketch of a Tree, Classification  problem  =====
+
+#FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8]
+
+
+
 !split
 ===== A typical Decision Tree with its pertinent Jargon, Classification Problem =====