Assume that we have 30 features each of which can potentially influence the result. I try to develop a model that uses some of the features. Each feature can be either considered or not. It means that in total we have `2^30` combinations.

Now we have considered several thousand combinations and for each of the combinations we have a measure of how it good. For example, know that if we take variable 1, 7, 13, and 27 we get an error (accuracy of the model) that is equal to 123.456. If we use variable 3, 4, 10, 11, 13, 27 and 29. We get an error equal to 23.456.

Now I would like to summarize this knowledge to be able to predict how use of arguments changes results. How can it be done?