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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 Miner ranks 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 is most useful in conjunction with Classification; the target for Attribute Importance in Filter Columns should be the same as the target of the classification model that you plan to build.
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:
Target, the value for which to find important attributes; usually the target of a classification problem.
Importance Cutoff, a number between 0 and 1.0; this value identifies the smallest value for importance that you want to accept. If the importance of an attribute is a negative number, then that attribute is not correlated with the target, so the cutoff should be non-negative. The default cutoff is 0. The rank or importance of an attribute allows you to select the attribute to be used in building models.
Top N, the maximum number of attributes; default is 100.
Select a Sample technique for the attribute Importance calculation. The default is Stratified; you can select Random.