Decision Tree
1

Introduction
A classification scheme which generates a tree and
a set of rules from given data set.
The set of records available for developing
classification methods is divided into two disjoint
subsets - a
training set
and a
test set
.
The attributes of the records are categorise into two
types:
Attributes whose domain is numerical are called
numerical
attributes.
Attributes whose domain is not numerical are called the
categorical attributes
.
2

Introduction
A decision tree
is a tree with the following properties:
An
inner node
represents an
attribute
.
An
edge
represents a
test
on the attribute of the father
node.
A
leaf
represents one of the
classes
.
Construction of a decision tree
Based on the training data
Top-Down strategy
3

Decision Tree
Example
The data set has five attributes.
There is a special attribute: the attribute
class
is the class label.
The attributes,
temp
(temperature) and
humidity
are numerical
attributes
Other attributes are categorical, that is, they cannot be ordered.
Based on the training data set, we want to find a set of rules to
know what values of
outlook, temperature, humidity
and
wind,
determine whether or not to play golf.
4

Decision Tree
Example
We have five leaf nodes.
In a decision tree, each leaf node represents a rule.
We have the following rules corresponding to the tree given in
Figure.
RULE 1
If it is sunny and the humidity is not above 75%, then play
RULE 2
If it is sunny and the humidity is above 75%, then do not play.
RULE 3
If it is overcast, then play.
RULE 4
If it is rainy and not windy, then play.
RULE 5
If it is rainy and windy, then don't play.
5

Classification
The classification of an unknown input vector is done by
traversing the tree from the root node to a leaf node.
A record enters the tree at the root node.
At the root, a test is applied to determine which child
node the record will encounter next.
This process is repeated until the record arrives at a leaf
node.
All the records that end up at a given leaf of the tree are
classified in the same way.