I think you're totally confused. Bias/variance relates how well your model fits relative to overfitting and underfitting. MSE is simply one of the many possible measures we can use to quantify how well a model performs.

Low bias (underfitting) can be measured by very high MSE and high variance (overfitting) can be measured by very low MSE.

I think you're totally confused. Bias/variance relates how well your model fits relative to overfitting and underfitting. MSE is simply one of the many possible measures we can use to quantify how well a model performs.

High bias can be measured by very high MSE and high variance can be measured by very low MSE.

I think you're totally confused. Bias/variance relates how well your model fits relative to overfitting and underfitting. MSE is simply one of the many possible measures we can use to quantify how well a model performs.

Low bias can be measured by very high MSE and high variance (overfitting) can be measured by very low MSE.

I think you're totally confused. Bias/variance relates how well your model fits relative to overfitting and underfitting. MSE is simply one of the many possible measures we can use to quantify how well a model performs.

Low bias (underfitting) can be measured by very high MSE and high variance (overfitting) can be measured by very low MSE.