Database Reference
In-Depth Information
other , or unknown . For this group, 1,763 out of 1,942 clients have not subscribed
to the term deposit.
The right node represents the rest of the population, for which the outcome of
the previous marketing campaign contact is a success . For the population of this
node, 32 out of 58 clients have subscribed to the term deposit.
This node further splits into two nodes based on the education level. If the
education level is either secondary or tertiary , then 26 out of 50 of the
clients have not subscribed to the term deposit. If the education level is primary
or unknown , then 8 out of 8 times the clients have subscribed.
The left node at depth 2 further splits based on attribute job . If the occupation is
admin , blue collar , management , retired , services , or technician , then
26 out of 45 clients have not subscribed. If the occupation is self-employed ,
student , or unemployed , then 5 out of 5 times the clients have subscribed.
7.1.2 The General Algorithm
In general, the objective of a decision tree algorithm is to construct a tree T from
a training set S . If all the records in S belong to some class C ( subscribed = yes,
for example), or if S is sufficiently pure (greater than a preset threshold), then
that node is considered a leaf node and assigned the label C . The purity of a
node is defined as its probability of the corresponding class. For example, in Figure
7.4 , the root
; therefore, the root is only
class. Conversely, it is 89.45% pure on the
10.55% pure on the
class.
In contrast, if not all the records in S belong to class C or if S is not sufficiently pure,
the algorithm selects the next most informative attribute A (duration, marital, and
so on) and partitions S according to A 's values. The algorithm constructs subtrees
,
… for the subsets of S recursively until one of the following criteria is met:
• All the leaf nodes in the tree satisfy the minimum purity threshold.
• The tree cannot be further split with the preset minimum purity threshold.
• Any other stopping criterion is satisfied (such as the maximum depth of
the tree).
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