Thanks for the comment Patrick. I agree that removing multicollinearity before completing any regression will provide better results and more robust model (I saw better results from Lasso regression model after removing multicollinearity independent variables). In addition, I googled some more over the last few days and found examples where the VIF factor is calculated in a loop and ...
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 feature selection?
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.
I'm aware that one of SHAP's disadvantages is the precision of SHAP values in scenarios with multicollinearity because of the assumption of predictor independence.
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 was not an issue for my models (all VIF values < 3). This was interesting, given that I had previously found high Pearson correlation between some predictors.
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 doing wrong with their model, and as if it's a reason to doubt what findings they do have.
I am trying to understand the basic difference between both . As per what i have read through various links, previously asked questions and videos - Correlation means - two variables vary togethe...
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 have too many variables
