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    $\begingroup$ thanks. I believe the coeff are returned back on the original scale. So one would need to re scale them (I assume by using the technique I posted for example). $\endgroup$ Commented Sep 1, 2011 at 18:18
  • $\begingroup$ user6129 is right! you don't get any means of ranking the variables selected. It's an active area of research. $\endgroup$ Commented Sep 1, 2011 at 18:31
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    $\begingroup$ @B_Miner: you are right, if called with "standardize=TRUE" glmnet returns coefficients on the original scale. One way to get around that is to standardize the explanatory variables outside (e.g. using "scale()" function) and call glmnet with "standardize=FALSE". The resulting coefficients could then be ranked by magnitude to judge their importance. $\endgroup$ Commented Sep 1, 2011 at 18:57
  • $\begingroup$ @suncoolsu: pls see my updated answer above $\endgroup$ Commented Sep 1, 2011 at 19:14
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    $\begingroup$ @Yevgeny I have a question. Then technically, should the performance results (e.g. area under the curve) be the same whether we set 'standardize=FALSE' and standardize the variables ourselves or we just use 'standardize=TRUE'? (Only the beta-coefficients returned would be different). This is what I theoretically think, but in practice, I get slightly better results when I use 'standardize=TRUE'. Hence, both the coefficients and performance are different. Is this how it should be? $\endgroup$ Commented Sep 11, 2017 at 6:21