Java Data Mining Concepts - Java Data Mining: Strategy, Standard, and Practice - page 151

1

Smith
Age=35

Jones
Age=41

Age > 36 Age <= 36

2 3

ATTRITER NON-ATTRITER

Smith
SB=$3000

SB < $21,500 SB >= $21,500

4 5

NON-ATTRITER ATTRITER

Figure 7-9 Apply decision tree to predict customer type.

based on average savings balance (SB). Because Smith's average
savings balance is less than $21,500, he is assigned to node-4, which
predicts him as a Non-attriter .
The classification apply operation enables generating prediction
results with various types of content such as the predicted category —the
target value predicted by the model; probability —the probability that
the prediction is correct according to the model; cost —the cost associ-
ated with the model's prediction (cost is computed only when cost
matrix is specified); and node-id —node or rule identifier used to make
the prediction (this is applicable only for models such as decision tree
that can provide a tree node or rule use for the prediction). In JDM the
apply prediction results can be presented in various forms, such as
the top prediction details, top-n or bottom-n predictions, probabilities
associated with all target values, and the probability of predicting a
specified target value(s). Selection of the prediction results depends
on the problem requirements and the type of information a user
wants to see. In this example, we produce the top prediction value
and its corresponding probability and cost to identify the attriters .
Even though applying a model to a dataset is common, predic-
tions and probabilities will likely change when customer attributes
change. For example, when a customer calls a bank to transfer a
large sum from his savings account to another bank, the call center
application can display a precomputed prediction that the customer
is likely to attrite. This would have been based on the customer's
previous account balance. With the funds transfer, this may change