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I have a regression issue and I would like to convert lots of categorical variables into dummy details, that may produce above two hundred new columns. Really should I do the function range ahead of this stage or soon after this move?
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Should really I do Function Selection on my validation dataset also? Or maybe do feature assortment on my training set by yourself after which do the validation using the validation set?
But I have some contradictions. For exemple with RFE I identified 20 attributes to pick however the feature the most important in Aspect Relevance is not really selected in RFE. How can we reveal that ?
I'm reaing your book device learning mastery with python and chapter 8 is about this matter and I have a question, ought to I use thoses complex with crude data or ought to I normalize info to start with?
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I did examination each scenario but benefits are different, exemple (initial situation column A and B are crucial but next circumstance column C and D are essential)
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