I don't agree with the parent, but I also don't think backfitting the theory is absurd.

What I've discovered from using machine learning in practice is that it's far more important to degrade gracefully when you have little data than to do the theoretically best thing when you have a lot of data. What this ends up meaning is that a hacky thing that is somewhat reasonable but based on the realities of the data will usually perform better than something more sophisticated that made too many simplifying assumptions along the way.

(That said, stats is amazing, and is the most important thing to learn for anyone getting into machine learning.)