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The goal of regression is to account for the variance in $y$. If you are able to do that, then your predictions of the conditional mean of $y$ (conditioned on the values of your features) will be better than if you were to predict $\bar y$ every time. When $R^2$ is positive, even if just slightly, it means that you are accomplishing that goal (assuming mode ...

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