# Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?

Normally we would remove features that have high pairwise correlation with another feature before performing regression. But is this step necessary if I am applying L2 regularized logistic regression (since the regularization algorithm would shrink the "irrelevant" feature coefficients to zero anyway)?

• Do you perhaps mean $L1$ (LASSO) regression? LASSO can (and often does) shrink coefficients to zero (not quite the same as feature selection...run a regression on just the "surviving" features and compare the coefficient estimates), but ridge regression would not be expected to shrink all the way to zero.