Table of Contents
The exercises in this chapter walk through the process of training, testing, and evaluating several probabilistic models designed for relational knowledge discovery. For each exercise in this chapter, we first walk through the source code and identify how Proximity classes and methods are used to accomplish the desired task. This is followed by instructions for executing the script in Proximity.
The examples in this chapter are written in Jython, a Java
implementation of Python that lets you interact with Java code. The
classes and methods used in these examples are, of course, also
available for use in Java code. Source code files for all the scripts
discussed in this chapter are available in
$PROX_HOME/doc/user/tutorial/examples.
Objectives
The exercises in this chapter demonstrate how to
learn, apply, and evaluate the relational Bayesian classifier model
learn, apply, and evaluate the relational probability tree model
learn, apply, and evaluate the relational dependency network model
use temporal attributes to restrict the set of applicable attribute values in a relational probability tree
display and interpret graphical representations of relational probability trees
display and interpret graphical representations of relational dependency networks