Representation & Importance
Hand-engineered features against learned representations, feature selection, and importance methods — with the warning every one of them needs: importance is not causality.
Either a person decides what the model sees, or the model decides. Each choice hides something, and the learned one ships inside the artifact and must be versioned like weights.
Fewer features means fewer serving dependencies, less leakage surface, and a smaller lie when the selection is done outside the training fold — which is where it is usually done.
Split gain, coefficients and every other model-specific importance answer one question: what does this model use? They do not answer what matters in the world, and unscaled coefficients do not even answer the first one.
Shuffle one column, re-score the model on held-out data, and the drop is what the deployed model depends on. Done on training data it measures memorisation; done one correlated feature at a time it splits the credit and hides the group.
Every importance and attribution method describes how a model's output depends on its inputs. None describes what would happen if you changed the world. "X predicts Y" and "X causes Y" are different claims, and the business hears the second.