Home Artificial Intelligence The Dummy Models of Scikit-learn What are dummy models? Dummy models in scikit-learn

The Dummy Models of Scikit-learn What are dummy models? Dummy models in scikit-learn

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The Dummy Models of Scikit-learn
What are dummy models?
Dummy models in scikit-learn

Should you like or need to learn machine learning with scikit-learn, take a look at my tutorial series on this amazing package:

Sklearn tutorial

All images by writer.

Dummy models are very simplistic models that should be used as a baseline to match your actual models. A baseline is just a few form of reference point to match yourself to. While you compute your first cross-validation results to estimate your model’s performance, you often know that the upper the rating the higher, and if the rating is pretty high on the primary try, that’s great. However it isn’t often the case.

What to do if the primary accuracy rating is pretty low — or lower than what you’d want or expect? Is it due to the info? Is it due to your model? Each? How can we all know quickly if our model isn’t badly tuned?

Dummy models are here to reply these questions. Their complexity and “intelligence” are very low: the concept is you could compare your models to them to see how significantly better you’re than the “stupidest” models. Note that they don’t intentionally predict silly values, they only take the simplest, very simplistic smart guess. Should you model gives worst performance than the dummy model, it’s best to tune or change your model completely.

A straightforward example for a dummy regressor can be to all the time predict the mean value of the training goal, regardless of the input: it’s not ideal, but on average it gives an inexpensive simplistic guess. In case your actual model gives worse results than this very, quite simple approach, it is advisable to review your model.

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