4.2.3 Building an Attribute Importance Model

The ore.odmAI function uses the Oracle Data Mining Minimum Description Length algorithm to calculate attribute importance. Attribute importance ranks attributes according to their significance in predicting a target.

Minimum Description Length (MDL) is an information theoretic model selection principle. It is an important concept in information theory (the study of the quantification of information) and in learning theory (the study of the capacity for generalization based on empirical data).

MDL assumes that the simplest, most compact representation of the data is the best and most probable explanation of the data. The MDL principle is used to build Oracle Data Mining attribute importance models.

Attribute Importance models built using Oracle Data Mining cannot be applied to new data.

The ore.odmAI function produces a ranking of attributes and their importance values.


OREdm AI models differ from Oracle Data Mining AI models in these ways: a model object is not retained, and an R model object is not returned. Only the importance ranking created by the model is returned.

For information on the ore.odmAI function arguments, invoke help(ore.odmAI).

Example 4-9 Using the ore.odmAI Function

This example pushes the data.frame iris to the database as the ore.frame iris_of. The example then builds an attribute importance model.

iris_of <- ore.push(iris)
ore.odmAI(Species ~ ., iris_of)
Listing for Example 4-9
R> iris_of <- ore.push(iris)
R> ore.odmAI(Species ~ ., iris_of)
ore.odmAI(formula = Species ~ ., data = iris_of)
             importance rank
Petal.Width   1.1701851    1
Petal.Length  1.1494402    2
Sepal.Length  0.5248815    3
Sepal.Width   0.2504077    4