Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners
REGRESSION ALGORITHM
There are a lot of times when my students come to me saying that they want to try the most sophisticated model out there for their machine learning tasks, and sometimes, I jokingly said, "Have you tried the best ever model first?" Especially in regression case (where we don't have that "100% accuracy" goal), some machine learning models seemingly get a good low error score but when you compare it with the dummy model, it's actually… not that great.
So, here's dummy regressor. Just like in classifier, the regression task also has its baseline model – the first model you have to try to get the rough idea of how much better your machine learning could be.
Definition
A dummy regressor is a simple machine learning model that predicts numerical values using basic rules, without actually learning from the input data. Like its classification counterpart, it serves as a baseline for comparing the performance of more complex regression models. The dummy regressor helps us understand if our models are actually learning useful patterns or just making naive predictions.
