I would greatly appreciate if you could let me know whether I should omit highly correlated features before using Lasso logistic regression (
L1) to do feature selection.
In fact, I want to use logistic regression with
L1 to do prediction as well as feature selection. However, some of my features are highly correlated e.g., -1 or 0.9. Should I omit them before applying Lasso or let the Lasso decide it?
Really, I read in Mr. Raschka’s book (Python Machine Learning) that
regularization is a very useful method to handle collinearity (high correlation among features).