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Dummy fashions are very simplistic fashions that are supposed to be used as a baseline to check your precise fashions. A baseline is just a few sort of reference level to check your self to. Once you compute your first cross-validation outcomes to estimate your mannequin’s efficiency, you normally know that the upper the rating the higher, and if the rating is fairly excessive on the primary strive, that’s nice. Nevertheless it isn’t normally the case.
What to do if the primary accuracy rating is fairly low — or decrease than what you’d need or anticipate? Is it due to the information? Is it due to your mannequin? Each? How can we all know rapidly if our mannequin isn’t badly tuned?
Dummy fashions are right here to reply these questions. Their complexity and “intelligence” are very low: the thought is that you would be able to evaluate your fashions to them to see how significantly better you might be than the “stupidest” fashions. Observe that they don’t deliberately predict silly values, they only take the simplest, very simplistic sensible guess. Should you mannequin provides worst efficiency than the dummy mannequin, you must tune or change your mannequin utterly.
A easy instance for a dummy regressor could be to at all times predict the imply worth of the coaching goal, regardless of the enter: it’s not splendid, however on common it provides an affordable simplistic guess. In case your precise mannequin provides worse outcomes than this very, quite simple strategy, you may need to assessment your mannequin.