Connection between bias/variance tradeoff and squared error The only explanations I've seen of the bias/variance tradeoff rely on rewriting the squared error of an estimator as the sum of bias and variance terms. How does the bias/variance tradeoff work if the loss function is not squared error? Thanks in advance!

@ loganecolss
Yes, we can not reduce both bias and variance simultaneously. Decrease in the one will lead to increase in the other, that's why we call it 'bias-variance trade-off'
