This section shows how
to use
Decision Tree nodes to create models,
and compare them with a
Model Comparison node.
A
Decision
Tree node can be used to classify observations based
on the values of nominal, binary, or ordinal targets. It can also
predict outcomes for interval targets or the appropriate decision
when you specify decision alternatives. An empirical tree represents
a segmentation of the data that is created by applying a series of
simple rules. Each rule assigns an observation to a segment based
on the value of one input.
One rule is applied
after another, resulting in a hierarchy of segments within segments.
The hierarchy is called a tree, and each segment is called a node.
The original segment contains the entire data set and is called the
root node of the tree. A node with all its successors forms a branch
of the node that created it.
The final nodes are
called leaves. For each leaf, a decision is made and applied to all
observations in the leaf. The type of decision depends on the context.
In predictive modeling, the decision is the predicted value. For more
information about
Decision Tree nodes, see
the SAS Enterprise Miner help.