Question Metrics on which observations are significant?

Hi,
I have a model that trains fairly well and that uses a few geometric observations. I have been trying to determine whether all the observations are significant for the model or whether some can be left out by retraining with different combinations of observations. Unfortunately, this is tedious and time consuming. Does the training process report any metrics on how significant each observation type is for the trained model? that would be extremely helpful in knowing whether some observations can be left out, and the impact of leaving them out if they do have some small contribution to the model.

A related question: Is there a downside to having some "unnecessary" observations or will the training process just learn to ignore some observations if they don't matter. I would guess that training will take longer if there are more observations.

I hope these questions are clear!

Thanks!

 

like your question, also would like to know the answer!