I was working on Dataset of Insurance in Python it had both categorical and numeric variables. For fitting a linear regression model I did the conversion of categorical to dummy variable, did scaling of whole data frame afterwards, after splitting training and testing data and, fitting model on training data, I found that VIF of one variable was more than 5 .(So while finding method, for reducing this high VIF, I came across Recursive Feature elimination method, in which it detects essential variables and nonessential variables). That particular variable with high VIF came under essential, so my query is should drop that variable by criteria of high VIF or keep it as it is? Is it only criteria for dropping variable?