Measuring impact of missing feature on model performance Is there any general approach to approximate how a missing feature will impact the prediction performance of a regression model? 

For example, if I train a model using 10 features, but want to make predictions afterwards only providing 8 of those 10 features - simply imputing the missing values -, how does that (statistically) affect my target metric. You could also think of it like some kind of confidence decline of the predictions. 

I wondered if there is some approach like deriving / estimating the deterioration of performance from feature importances, or similar. I did not find any resources about that though, nor could I come up with some meaningful idea myself so far.
I would just remove each feature individually and see how that effects performance on my test set.  No need to get fancy.