Attribute Importance

If a data set has many attributes, it is likely that not all attributes contribute to a predictive model. Indeed, some attributes may simply add noise, that is, they actually detract from the model's predictive value. Oracle Data Mining provides Attribute Importance (AI) that uses the Minimum Description Length (MDL) algorithm to rank the attributes by significance in determining the target value. You can then filter out attributes that are not important in determining the target value.

Using fewer attributes does not necessarily result in lost predictive accuracy. Using too many attributes (especially those that add noise) can affect the model and degrade its performance and accuracy. Mining using the smallest number of attributes can save significant computing time and may build better models.

Attribute Importance calculates rank and importance for each attribute. The rank of an attribute is an integer. Importance is a real number that may be negative.

Specify theses value for attribute importance: