Data Usage

The Data Usage tab is not supported for the Association node. To modify any values, to see which attributes are not used as input, or to see mining types, select View in the lower pane.

You can change data usage information for several models at the same time.

The Data Usage tab contains the data grid. The data grid lists all attributes in the data source. For each attribute, the grid lists displays the following:

• Name: This is the name of the attribute.

• Data Type: This is the Oracle Database data type of the attribute.

• Input: Indicates if the attribute is used to build the model. To change the input type, click Automatic. Then click the icon and select the new icon. For models that have a target, such as Classification and Regression models, the target is marked with a red target icon.

  • The icon indicates that the attribute is used to build the model.

  • The icon indicates that the attribute is ignored, that is, it is not used to build the model.

• Mining Type: This is the logical type of the attribute, either Numerical (numeric data), Categorical (character data), nested numerical, or nested categorical, text or custom text. If the attribute has a type that is not supported for mining, then the column is blank. Mining type is indicated by an icon. Move the cursor over the icon to see what the icon represents.
  To change the mining type, click Automatic and then click the type for the attribute. 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.

• Auto Prep: If Auto Prep is selected, then automatic data preparation is performed on the attribute. If Auto Prep is not selected, then 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.

• Rules: After a model runs, Rules describe the heuristics used. For details, click Show.

There are two types 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, O-Cluster does not support nested data types such as DM_NESTED_NUMERICALS. If you use an attribute with type DM_NESTED_NUMERICALS to build a O-Cluster model, then 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, then the model has lower quality than if you exclude them.