# Is it advisable to impute missing values and scale features before computing the Variance Inflation Factor (VIF)?

As far as scaling, Wikipedia says:

Finally, note that the VIF is invariant to the scaling of the variables (that is, we could scale each variable Xj by a constant cj without changing the VIF).

But that means that all the variables would need to be scaled by the same constant, which is not how feature scaling is usually done.

To reiterate, VIF is calculated as 1/(1-R^2); R^2 (R-square) is obtained from the OLS model fitted for each numeric feature as a dependent variable and rest all features (leaving the actual target variable) as predictors. R^2 of say 0.65 implies that the predictors used in the OLS model can explain the linear behavior of the dependent variable up to 65%.