Home > Model Nodes > Online Help for Model Nodes > Association Node > Classification Node > Advanced Settings Overview > Lower Pane of Advanced Sett... > Data Usage Tab
The data usage tab is not supported for Association.
To modify any values, to see which attributes are not used as input, or to see mining types, unselect Auto in the upper pane.
You can change data usage information for several models at the same time, as described in Viewing and Changing Data Usage.
The data usage tab contains the Data Grid. The Data Grid lists all attributes in the data source. For each attribute, the grid lists Data Type, Input, Mining Type, and Auto Prep.
Data Type is the Oracle Database data type of the attribute.
Input is selected if the attribute is used to build the model. To see which attributes are used as input, unselect Auto in the Models Grid in the top pane. Once you unselect Auto, you can change selections. Use this tab to deselect (ignore) attributes if you have unselected Auto.
Mining Type is the logical type of the attribute, either Numerical (numeric data), Categorical (character data), nested numerical, or nested categorical. If the attribute has a type that is not supported for mining, the column is blank. To see the mining type of an attribute, unselect Auto in the Models Grid in the top pane; you can change mining types.
To change the mining type, click the type for the attribute and select a new type from the list. You can change mining types as follows:
Numerical can be changed to Categorical; changing to Categorical casts the numerical value to string
Categorical
Nested Categorical and Nested Numerical cannot be changed.
Input, if selected, indicates that the attribute is used to build the model. To see which attributes are input, unselect Auto in the Models grid. You can change the selections.
There are two kinds of reasons for not selecting an attribute as Input:
The attribute has a data type that is not supported by the algorithm used for model build.
For example, Decision Tree and O-Cluster do not support nested data types such as DM_NESTED_NUMERICALS
; if you use an attribute with type DM_NESTED_NUMERICALS
to build a Decision Tree or O-Cluster model, the build fails.
The attribute does not provide data useful for mining. For example, an attribute that has constant or nearly constant values.
If you include attributes of this kind, the model has lower quality than if you exclude them.
If Auto Prep is selected, Automatic Data Preparation is performed on the attribute. If Auto Prep is not selected, no automatic data preparation is performed for the attribute; in this case, you are required to perform any data preparation, such as normalization, that may be required by the algorithm used to build the model. No data preparation is done (or required) for target attributes. The default is to perform automatic data preparation.