Is it possible for a feature not correlated (or faintly correlated) with a dependent variable to become important in a machine learning model?
Suppressor variables are an example. Two good explanations are here and here but you can search and find a bunch more references.
A common view is a suppressor sharpens another variables relationship to the target. The suppressor is correlated with another feature and not the target but the correlation is in the area of the residuals of the other feature to the target.
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