Hi there,
I am currently learning Enterprise Miner 4.3 (SAS 9.2), and I have a question around the Transform Nodes.
In one of our exercises our lecturer told use to use the transform node to apply different transformation to a variable in order to get the best result (the best result would be a variable with less skewness than the original variable). One thing I noticed however is that our lecturer only tried log and sqrt transformations. I was wondering if there would be any implications of using the inverse transformation ans I managed to significantly reduce the skewness of a variable using that transformation.
Are there any caveats I should know?
Regards,
P.
PS: I am also curious the the position of the transform and replacement model (one next to the other) could have any significant impact in a model.
You should feel free to try anything you want. In data mining, it is not uncommon to try dozens of transformations of the same variable, and then select those that seem to work best. EM offers a series of "Best Power" transformations (Maximum Normality, Maximum Correlation with Target,...), and each one will try for you several transformations: x, log(x), sqrt(x), e^x, x^(1/4), x^2, x^4.
There isn't a rule regarding whether to do transformation, selection or imputation in a specific order. Try different things, and see what works best. EM makes it so easy that I don't see a reason why you wouldn't want to try different approaches just for the sake of curiosity.
G
You should feel free to try anything you want. In data mining, it is not uncommon to try dozens of transformations of the same variable, and then select those that seem to work best. EM offers a series of "Best Power" transformations (Maximum Normality, Maximum Correlation with Target,...), and each one will try for you several transformations: x, log(x), sqrt(x), e^x, x^(1/4), x^2, x^4.
There isn't a rule regarding whether to do transformation, selection or imputation in a specific order. Try different things, and see what works best. EM makes it so easy that I don't see a reason why you wouldn't want to try different approaches just for the sake of curiosity.
G
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