DECISION TREE EXAMPLES
Decision tree structure and nodes vary depending on the object of data mining
and on the structure of information you possess.
Below there are three decision tree examples that will help you
understand the idea of decision tree structure and application.
DECISION TREE Example #1
To understand the decision tree structure lets start with a simple decision
Lets suppose we have some information about three objects: a box, a ball, and
a clew. The goal is to create a decision tree using which we will be able to
define: whether the object is a box, a ball, or a clew? Such a process is called
The initial information is:
- objects form - is it round (Yes/No)
- structure - does it contain threads (Yes/No).
Lets create a decision tree that will determine what an object is. In case if
we select "objects form" as a root node, we will receive:
In other case the decision tree will start with "structure":
In both examples we can see a path from the root node up to the final node.
Now, the created tree can be used for classifying objects: moving through the
tree nodes, we will be able to determine objects type. For example: if (the
object is round= Yes) then if (does it contain threads = No) then (Object Class
These decision tree examples might seem too easy, but it is important to understand,
that just as classifying such simple objects, you will be able to classify your
clients, business processes, marketing campaigns and many other vital
information - a power to forecast your business future.
DECISION TREE Example #2 - decision tree IN marketing
One of well-know decision trees application is
The goal is to create an extremely effective targeted marketing campaign with
maximized return on investment (ROI).
DECISION TREE Example #3 - decision tree IN INSURANCE
Decision trees method can be used not only in marketing, but in any other
field that involves decision making process. And if in case of marketing
campaign, we will need a decision tree from time to time, this doesn't mean that
this data mining method cannot be of everyday use.
A good example of possible everyday use are decision tree in insurance
industry. In this case, the key question is: "does the client belong to the
reliable, not fraudulent group?"
The initial information can be :
- sex (M/F)
- age (under 25, 26-35, 36-45, 46-55, over 56)
- education (None, High)
- marital status (Yes(married)/No(not married))
- real estate (Yes(is owner)/No(doesn't own))
- approximate annual revenue
- belonging to fraudulent group
A part of the decision tree could look like this:
This kind of decision tree allows quickly analyze to what group a client
might belong. Such analysis of clients database will help with making the
decision about insuring risks. Now the decision can be based not only on intuition,
but on reliable facts.
Please remember, that these are only decision tree examples. These
examples can not be used for analysis of your databases.
To receive your
own decision tree examples use ESTARD Data Miner.