I have a question related to ranking of features after modeling step. I had total 1204 features in original predictor file. After doing elastic net modeling(using cv.glment package), out of 1204 128 features got selected. I want to see the ranking of these features. Like out the selected features, which one has higher rank in explaining the variance? I tried to do backward algorithm, wherein I removed one feature at a time and then do remodeling with n_1 feature and looked at correlation value. But in my case for certain feature its not giving good correlation. For example: if feature 1 in model has effect size of 1.573 its removal during model and then doing correlation should be lower as compared to correlation with its presence. But in my case its removal doesn't affect the correlation at all.
Does anyone know of any method/algorithm that can be used to rank features after modeling and then do statistics to see its performance? Thank you.