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I am reading a paper that has a wide number of explanatory variables. In order to reduce this number, the author first computes the (Pearson) correlation between each pair and keeps just one of the high correlated ones. This way, he reduced the number of variables and then feed an ML model with it.

My question: from what I understand, there are some ML techniques that 'are able' to identify no linear relationships. Is it ok to employ linear correlation to dismiss some variables because of their LINEAR relationship to then use a model which not necessarily models a linear relationship?

Can somebody help me to clarify this doubt? Thank you!

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    $\begingroup$ Welcome to Cross Validated! This kind of univariate screening is problematic, even setting aside the possibility of a nonlinear relationship. That paper appears to be using poor practices. $\endgroup$
    – Dave
    Jun 28 at 17:30

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