Model details reveal information about model attributes and their treatment by the algorithm. There is a separate GET_MODEL_DETAILS routine for each algorithm.
Transformation and reverse transformation expressions are associated with model attributes. Transformations are applied to the data attributes before the algorithmic processing that creates the model. Reverse transformations are applied to the model attributes after the model has been built, so that the model details are expressed in the form of the original data attributes, or as close to it as possible.
Reverse transformations support model transparency. They provide a view of the data that the algorithm is working with internally but in a format that is meaningful to a user.
Example 3-3 shows the definition of the GET_MODEL_DETAILS function for an Attribute Importance model.
Example 3-3 Model Details for an Attribute Importance Model
The syntax of the GET_MODEL_DETAILS function for Attribute Importance models is shown as follows.
DBMS_DATA_MINING.GET_MODEL_DETAILS_AI (
model_name VARCHAR2)
RETURN DM_RANKED_ATTRIBUTES PIPELINED;
The function returns DM_RANKED_ATTRIBUTES, a virtual table. The columns are the model details. There is one row for each model attribute in the specified model. The columns are described as follows.
attribute_name VARCHAR2(4000) attribute_subname VARCHAR2(4000) importance_value NUMBER rank NUMBER(38)