How to deal with multicollinearity when performing variable selection? Ask Question Asked 13 years, 7 months ago Modified 6 years, 2 months ago

Mar 2, 2021 · Lasso I am applying Lasso regression as the model can detect multicollinearity and thus reduce the variable coefficients to 0. I have normalised all dependent variables in the …

Jul 15, 2021 · 12 Does it ever make sense to check for multicollinearity and perhaps remove highly correlated variables from your dataset prior to running LASSO regression to perform …

multicollinearity refers to predictors that are correlated with other predictors in the model It is my assumption (based on their names) that multicollinearity is a type of collinearity but not sure.

The blogger provides some useful code to calculate VIF for models from the lme4 package. I've tested the code and it works great. In my subsequent analysis, I've found that multicollinearity …

Mar 23, 2025 · I am new to the subject and only know from cross-sectional linear regression models that variance inflation factors (VIFs) can be a great way to detect multicollinearity in …

Mar 13, 2015 · In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I …

Sep 27, 2014 · A question about multicollinearity for random forests has been asked and answered, but what about boosted trees?

Oct 11, 2017 · Also, When having perfect multicollinearity, why does dropping the intercept help us avoid it? What I mean is that when two regressors are in a linear relationship for some …

Multicollinearity is the symptom of that lack of useful data, and multivariate regression is the (imperfect) cure. Yet so many people seem to think of multicollinearity as something they're …