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
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.
Scott
Dear Kian,
there is nothing wrong with it. Forward and Backward Selection have to deliver different results.
Best,
Martin
Dortmund, Germany
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
nice link, @IngoRM
Scott