Modeling and Multivariate Methods - SAS

Chapter 13 Recursively Partitioning Data 317
Introduction to Partitioning
Introduction to Partitioning
Variations of partitioning go by many names and brand names: decision trees, CART TM , CHAID TM , C4.5,
C5, and others. The technique is often taught as a data mining technique because
• it is good for exploring relationships without having a good prior model,
• it handles large problems easily, and
• the results are very interpretable.
A classic application is where you want to turn a data table of symptoms and diagnoses of a certain illness
into a hierarchy of questions to ask new patients in order to make a quick initial diagnosis.
The factor columns (X’s) can be either continuous or categorical (nominal or ordinal). If an X is continuous,
then the splits (partitions) are created by a cutting value. The sample is divided into values below and above
this cutting value. If the X is categorical, then the sample is divided into two groups of levels.
The response column (Y) can also be either continuous or categorical (nominal or ordinal). If Y is
continuous, then the platform fits means. If Y is categorical, then the fitted value is a probability. In either
case, the split is chosen to maximize the difference in the responses between the two branches of the split.
For more information on split criteria, see “Statistical Details” on page 348.
Launching the Partition Platform
To launch the Partition platform, select Analyze > Modeling > Partition. The Partition launch window is
shown in Figure 13.2, using the Boston Housing.jmp data table.
Figure 13.2 Partition Launch Window