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).
However, this kernel (by referring to Wikipedia) states that keeping correlated features in the model would adversely affect the feature selection but it doesn't impair the predictions.