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Dummy fashions are very simplistic fashions that are supposed to be used as a baseline to match your precise fashions. A baseline is just a few sort of reference level to match your self to. Whenever 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 attempt, that’s nice. But it surely 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 you can evaluate your fashions to them to see how a lot better you might be than the “stupidest” fashions. Be aware that they don’t deliberately predict silly values, they only take the best, very simplistic good guess. In case you mannequin provides worst efficiency than the dummy mannequin, you need to tune or change your mannequin utterly.

A easy instance for a dummy regressor can be to all the time predict the imply worth of the coaching goal, regardless of the enter: it’s not superb, however on common it provides an inexpensive simplistic guess. In case your precise mannequin provides worse outcomes than this very, quite simple method, you would possibly wish to evaluate your mannequin.