different output from same oprator in Rapidminer, for forward and backward feature selection
Hi I want to doing feature selection with rapidminer with forward selection and backward elimination. there are three operator: 1:optimize selection(forward and backward), 2: optimize weight forward and backward and 3: forward selection and backward elimination operators. all there operator needs to have a inner operator like cross-validation operator toe evaluate the model. But in output of these three operator there are different selected feature and different accuracy. also when i do a cross validation separately with selected attributes by select attribute operator without using the forward and backward operator and with only use the their selected attributes, there are different results. i dont know whats wrong here! anyone can help? thanks
Answers
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hello @Kian welcome to the community! I'd recommend posting your XML process here (see https://youtu.be/KkgB5QXWXJ8 and "Read Before Posting" on right when you reply) and attach your dataset. This way we can replicate what you're doing and help you better.
Scott0 -
Dear Kian,
there is nothing wrong with it. Forward and Backward Selection have to deliver different results.
Best,
Martin
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If you want to learn more about why they typically deliver different results, I would recommend to check out this blog post here: https://community.rapidminer.com/t5/RapidMiner-Studio-Knowledge-Base/Multi-Objective-Feature-Selection-Part-1-The-Basics/ta-p/45775
Cheers,
Ingo
1
Altair Employee
