How do you address multicollinearity in regression models?

Question

Anonymous · Accepted Answer

Multicollinearity between features is when 2 or more independent features are
correlated. Certain models such as linear regression assumes that underlying
data has no Multicollinearity. We can use VIF to see if there is any
Multicollinearity in the data. we can remove features with VIF value >5 to
address Multicollinearity

How do you address multicollinearity in regression models?

Practice this question with AI

Go Premium

Community Answers

Unlock Full Access

Practice More Questions