# Identifying importance of each feature in deep model

I have a deep model and I want to figure out which feature has the maximum influence on predicted result. For this I train the model with all the features I think are important, during prediction I set all features =0 one by one keeping the rest unchanged so I could figure out which is the least important of all. On predicting the results of these tampered test set on the trained model I get the same(changes at 5th-6th decimal place) F1 score, Recall and Precision.

Can some one explain me where I am wrong?

• I have changed a few features simultaneously as you suggested and my results have varied (on 2nd decimal place). Just one more thing, I just realized most of the entries in my dataset are already =0 and therefore the results don't vary as much as they should. What value shall I set my features to that signify removing it other than 0. Any idea? Jul 24 '19 at 1:42
• that appears a nice idea! I will try this. Another query, why you suggest (mean=0, std=feature's std)? Jul 24 '19 at 2:05