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"automatic feature engineering sample process with demonstrative results"

Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn
edited May 2019 in Help
I've been playing with the new Automatic Feature Engineering operator, but most of the discussion I have seen so far has centered around the Feature Selection components of its capabilities.  Is there any sample process or further documentation/discussion available for the feature engineering aspects of the operator?  The tutorial process doesn't include feature engineering (and enabling it doesn't produce any new attributes), and the in-program help doesn't discuss the feature engineering options or parameters either.  Even when I have applied this operator to other sample datasets (the usual suspects: Titanic, Sonar, etc.) I haven't been able to get it to generate anything useful.  Any sample processes on available datasets (perhaps those used in development or other suggestions) would be appreciated. Thanks.

Brian T.
Lindon Ventures 
Data Science Consulting from Certified RapidMiner Experts
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    lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn
    Hi again Brian,

    To complete my previous post, you can play with the balance for accuracy parameter : 
    For example by increasing its value to 1, AFE will generate a new set of attributes, will increase the global complexity of the set and in fine
    decrease the final "fitness function". (vs the parameters setting of the process in my previous post)

    Regards,

    Lionel 
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    Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn
    Thanks guys, this definitely helps.  I appreciate the concrete examples.
    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
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