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 …

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 …

Explore related questions r correlation mixed-model lme4-nlme multicollinearity See similar questions with these tags.

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 …

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.

Apr 8, 2025 · 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. This …

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 …

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 …