# Comparison of regression models in terms of the importance of variables

I would like to compare models (multiple regression, LASSO, Ridge, GBM) in terms of the importance of variables. But I'm not sure if the procedure is correct, because the values ​​obtained are not on the same scale.

In multiple regression and GBM values ​​range from 0 - 100 using varImp from the caret package. The calculation of this statistic is distinct in each of the methods.

Linear Models: the absolute value of the t-statistic for each model parameter is used.

Boosted Trees: this method uses the same approach as a single tree, but sums the importance of each boosting iteration.

While for LASSO and Ridge the values ​​are from 0.00 - 0.99, calculated with the function:

varImp <- function (object, lambda = NULL, ...) {
beta <- predict (object, s = lambda, type = "coef")
if (is.list (beta)) {
out <- do.call ("cbind", lapply (beta, function (x)
x [, 1])))
out <- as.data.frame (out)
} else
out <- data.frame (Overall = beta [, 1])
out <- abs (out [rownames (out)! = "(Intercept)",, drop = FALSE])
out
}


Which was obtained here: Caret package - glmnet variable importance

I was guided by other questions on the forum, but could not understand why there is the difference between the scales. How can I make these measurements comparable?