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Indentifying and eliminating insignificant attributes
Contributor I
Hello!
Within statistics we usually operate with a 5% significance value for determining which attributes are significant or insignificant in the model. Thereby one can remove insignificant attributes and get a "simpler" model. How would one go ahead doing this in Rapidminer and is there an automated process for it?
Within statistics we usually operate with a 5% significance value for determining which attributes are significant or insignificant in the model. Thereby one can remove insignificant attributes and get a "simpler" model. How would one go ahead doing this in Rapidminer and is there an automated process for it?
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I'm not aware of an implementation of the feature your describe in RapidMiner.
But more generally, what you describe - get a "simpler" model - is called feature selection.
In RapidMiner you can apply feature selection by :
- Enabling Automatic Feature Selection and choosing your option (simple/balanced/accurate) in Auto-Model.
- Using directly in your process the Automatic Feature Engineering operator.
- Using the feature weights operator(s) (Weight by Information Gain, Weight by Correlation etc.) associated with the Select by Weights operator.
Hope this helps,
Regards,
Lionel