Can all types of ML methods benefit from bagging? Decision Tree Classification seems always be the go-to example of bagging, what about other classifiers or regressions?
When it's suitable to do bagging, how to pick the size and number of bags?
I am looking for something mathematically more rigorous, such as, for each model (single learner) we can break its total estimation error into:
$Error^2 = Bias^2 + Variance^2 + Irreducible^2$
If we can have a rough estimation of $Variance$ and the correlation between the predictions from all the single learners, then we can know about how low we can push variance to through ensemble.