r2 for regression models it is a score or error?
some places I have seen it is called as score and some other place as error. Lets suppose r2=0.83 means that score = 83% and Error= 17% or vise versa
Topic score regression
Category Data Science
R2 of 0.83 means that approx 83% of the variance can be explained by the model, 17% of the variance can't be explained by the model.
This is one of the measures to evaluate a model, but you shouldn't try and maximize any number, just for the sake of maximizing the number. An high R2 could be a sign of over fitting, and a model with low R2 could still be useful.
But in a simplistic way, yes they could be seen as "score" and "error". But the implementations of the number can vary.
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