# Solved – this measure called: MSE divided by variance of dependent variable

When evaluating a regression model with cross-validation I thought that the meaningful measure would be MSE divided by the MSE of the null model which consists of always predicting the mean,
\$frac{hat E[(y-hat{y})^2]}{hat E[(y-bar{y})^2]}\$. This is 1 if the model does not add anything, and 0 if the prediction is perfect (and can even be greater than 1 if the model is actively harmful), To make it more interpretable, I can flip it around:

\$1 – frac{hat E[(y-hat{y})^2]}{hat E[(y-bar{y})^2]}\$.

This can even be negative if the prediction is worse than the null model.

I have seen people use

\$1 – frac{rm{var}(y-hat{y})}{rm{var}(y)}\$

and calling it explained variance, but this seems too generous for the model as it does not penalize it for additive or multiplicative biases.

What is the measure I have above called? Is there a reason why it is not used or have I just missed the relevant examples?

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