[D] For samples of subsets, features are predictive in gradient boost algorithm.
I’m doing a project with my professor. We are analyzing impact of each feature on model performance, mostly we do it for gradient boost. Professor told me that for samples of subsets features are predictive and it is a problem that we can observe this by analyzing the shallow trees created during the process.
What does “samples of subsets features are predictive” means? I have been searching internet but couldn’t find anything. Any ideas?