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or you should counsel me Another technique for this sort of dataset (ISCX -2012) in which concentrate on course is categorical and all other characteristics are continual.
But just after knowing the significant features, I'm not able to produce a design from them. I don’t know how to giveonly those featuesIimportant) as enter for the product. I necessarily mean to mention X_train parameter may have many of the functions as enter.
That could be a lot of new binary variables. Your ensuing dataset will be sparse (plenty of zeros). Aspect selection prior might be a good suggestion, also try out right after.
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The moment I acquired the decreased version of my facts due to applying PCA, how can I feed to my classifier?
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Your code is suitable and my result is the same as yours. My level would be that the most effective characteristics observed with RFE are preg, mass and pedi.
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