Java Data Mining Concepts - Java Data Mining: Strategy, Standard, and Practice - page 142

Listing 7-2 Naïve bayes algorithm computing probability for a new case
Probability that customer is attriter given age 35 and
savings balance (SB) $13,300
P(Attriter / age 25 and SB $13,300)
P(age < 35/Attriter) P(SB < $20000/Attriter) P(Attriter)
0.33

0.43 0.2 0.03

Probability that customers is Non-attriter given age 35 and
savings balance $13,300
P(Non-attriter / age 25 and SB $13,300)
P(age < 35/Non-attriter) P(SB < $20000/Non-attriter) P(Non-attriter)
0.67

0.57 0.8 0.31

When a naïve bayes model is built, a given value of a predictor
attribute is ignored unless there are enough occurrences of that
value. The frequency of occurrences in the build data must equal or
exceed the fraction specified by the singleton threshold. For example,
when a singleton threshold of 0.001 is specified and age 15

occurred only 10 times out of 100,000 cases, then age 15 is ignored

because (10/100000 0.0001) < 0.001.

Similarly, a pair of values between a predictor and target
attribute is ignored unless there are enough occurrences of that pair
in the build data. The frequency of occurrences in the build data
must equal or exceed the fraction specified by the pairwise threshold.
For example, when a pairwise threshold of 0.01 is specified and the
pair age 25 and target value Attriter occurred 2,000 times out of

100,000 cases, then age 25 is used by the model because (2000/

100000 0.02) > 0.01.

Support Vector Machine
The support vector machine (SVM) algorithm is one of the most
popular, relatively new supervised algorithms. SVM is proven to
give highly accurate results in complex classification problems, such
as gene expression analysis in which the number of known cases is
small but the number of attributes can be quite large [Brown

2000].
SVM is gaining greater acceptance in solving traditional data mining
problems [DM Methods Poll 2006], including being a preferred
alternative to neural networks.

Overview
The SVM algorithm creates a hyperplane that separates target values
with a maximum-margin . A hyperplane is the plane that divides a
space into two spaces. For example, in two-dimensional space, as