Here's the scene I've watched play out on a dozen teams. Accuracy plateaus. Someone rips out the logistic regression, drops in XGBoost, and waits for the jump. It doesn't come — or it comes with two points you can't explain to anyone. So the week disappears into hyperparameter tuning, and you end up with a slower, heavier, less interpretable model that's barely better than where you started.
The model was almost never the bottleneck. The features were.
This post is the long, practical version of that argument. We'll define the two camps in plain language, run real code, look at when boosting genuinely wins, and then walk through the failure mode nobody warns you about — the one where the fancy model is "winning" because it's quietly cheating.
A note before we start: keep your examples generic. We'll predict a numeric target — think demand, a quantity, a score on a tabular dataset. The principles are the same everywhere, and you should validate them on your own data.
The two camps, in plain terms
