just clarifying : categorical data features interaction

[Sandy4321]

great code (as what you doing )

"An important feature is that dummy variables are handled properly, so that for instance powers of a dummy variable do not exist as duplicates of the original."

just clarifying
given categorical data

f1 f2 f3
a c u
b g x
a k y

after features interaction will it be for row N1 ?

f1 f2 f3 f1 f2 f3 f1 f2 f1f3 f2f3 f1f2f3
a c u => a c u ac au cu acu

to make sure
there will not be ua, ca , ... uca , etc

[matloff]

Thanks for the note. Not sure what you mean? Have you tried constructing a small example and then calling getPoly()? Norm

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On Sun, Jul 24, 2022 at 11:53:18AM -0700, Sandy4321 wrote: great code (as what you doing ) "An important feature is that dummy variables are handled properly, so that for instance powers of a dummy variable do not exist as duplicates of the original." just clarifying given categorical data f1 f2 f3 a c u b g x a k y after features interaction will it be for row N1 ? f1 f2 f3 f1 f2 f3 f1 f2 f1f3 f2f3 f1f2f3 a c u => a c u ac au cu acu to make sure there will not be ua, ca , ... uca , etc — Reply to this email directly, [1]view it on GitHub, or [2]unsubscribe. You are receiving this because you are subscribed to this thread. Message ID: ***@***.***> References 1. #26 2. https://github.com/notifications/unsubscribe-auth/ABZ34ZI76MQPRQDNJD5FYP3VVWGJ3ANCNFSM54QCKMSA

[Sandy4321]

I ask you confirm if it works properly with categorical values
What behavior to expect
For long time I am looking for some theoretically correct design for categorical values features interaction

[Sandy4321]

by the do you remove NEW muti-collinear (similar ) FEATURES
since due to original features multiplication many very similar columns may be detected