I am listening to lesson 5 and I am not sure I understand the extrapolation section. So you try to predict your validation set records (in my case, I have a holdout set) Then you take the feature importance of those and try to drop each of them and run the model like that. At that point I would expect you to keep the columns that would make the score worse if it weren’t in the model and drop anything that makes it better, but Age which when dropped doesn’t hurt but you still keep it in. Why is this not also dropped? I have tried implementing this in a real world scenario and I am not getting any of my columns that are making the model better when they shouldn’t, but when I predict a previous month and remove all the data after that point, I get fairly decent results, but when I try to predict the following month, I am not getting as good of results. I suspect data leakage of some sort, but I haven’t tracked it down yet.