title: Lasso regression won't remove 2 features which are highly correlated
body: <p>I have two features, say F1 and F2 which has a correlation  of about 0.9.</p>
  
  <p>When I built my model, I first considered all the features to go into my regression model. Once I have my model, I then ran Lasso regression on my model, with the hope that this will tackle any colinearity between the features. However, the Lasso regression kept both F1 and F2 in my model.</p>
  
  <p>Two questions:</p>
  
  <p>i) If F1 and F2 are highly correlated, but Lasso regression still kept both of them, what could this mean? Does it mean regularization doesn't work in some cases?</p>
  
  <p>ii) How do I adjust my model or the Lasso regression model to kick out F1 or F2 in my model? (I am using <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow noreferrer">sklearn.linear_model.LogisticRegression</a>, and have set penalty = 'l1' or ‘elasticnet’, tried very large or very small C values, tried 'liblinear' or 'saga' solvers, and l1_ratio = 1, but I still can't kick out either F1 or F2 from my model)</p>