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I know that feature selection helps in removal of irrelevant features. Do they also remove redundant features(features which convey the same info as as some other feature)? If not, which methods are used for removing these highy corelated variables?

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Lasso is a very common strategy as it is able to handle correlated variables.

https://stats.stackexchange.com/questions/7935/what-are-disadvantages-of-using-the-lasso-for-variable-selection-for-regression

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