I am developing a prognostic index using the LASSO technique and wondering how to deal with the highly correlated predictor variables. Should I choose the ones I want to include in the LASSO a priori or is the LASSO programmed to select the best/better of highly correlated predictors variables? thanks.

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    $\begingroup$ That is one of LASSO inherit weakness. You could probably use elastic net to make use of a ridge penalty to make feature slightly "more uncorrelated". This post on: How does LASSO select among collinear predictors? is rather enlightening too, I think. $\endgroup$
    – usεr11852
    Jul 26, 2016 at 9:19


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