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Suppose I have a response variable y and and a set of feature variables (x1, x2 ... xn). I wish to find which of x1...xn are the best features for y in a regression problem (the relationship might not be linear).
Is there any way I can do this kind of feature selection without using any correlation measure or regression function in the process (i.e. I cannot use any filter or wrapper methods)?

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  • $\begingroup$ correlation measures linear relationships anyway. $\endgroup$ – Nikos M. May 26 at 12:14
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You can train a LightGBM Regressor. LightGBM Regressor feature selection methods embbed in it. You can direct plot them to see which features are important. See this link. LightGBM Plot Importance

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Google up scikit learn feature selection. You should look to use the f_regression and mutual_info_regression to identify the best features for the problem at hand.

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