# bias-variance tradeoff vs precision and recall

Can anyone explain the link between bias-variance tradeoff and precision-recall tradeoff. Are they effectively the same thing?

$MSE(W) = Bias^2(W) + Var(W)$
where, $Bias(W) = E[W] - \theta$, if $\theta$ is the true parameter.
Now moving on to precision and recall, which are related to minimizing false positives and false negatives respectively. In the extreme case, you could have a classifier which simply remembers the training set, in this case you would have a recall close to or even equal to $1$ and a precision close to $0$. A high recall and low precision model corresponds to the case of having high variance and low bias. Similarly you could have a model which gets some false negatives but gets fewer false positives, ie, it is high precision - low recall, then it corresponds to the high bias - low variance case.