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My advice is to test almost everything you could imagine and find out what gives the top final results on your own validation dataset.
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Statistical tests can be employed to choose those functions which have the strongest romance with the output variable.
I was asking yourself if I could Develop/educate One more model (say SVM with RBF kernel) utilizing the characteristics from SVM-RFE (whereby the kernel made use of is often a linear kernel).
No, it's essential to pick the amount of characteristics. I would recommend utilizing a sensitivity Examination and try a range of various features and see which ends up in the top carrying out model.
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I observed that when you use a few attribute selectors: Univariate Collection, Aspect Great importance and RFE you obtain unique result for three vital characteristics. 1. When using Univariate with k=3 chisquare you obtain
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