# Bias variance tradeoff boosting (xgboost) vs random forest (randomized bagging) which to use when?

I was looking up differences between boosting an bagging and I see this quoted everywhere

If the classifier is unstable (high variance), then we should apply Bagging. If the classifier is stable and simple (high bias) then we should apply Boosting.

also

Breiman [1996a] showed that Bagging is effective on unstable'' learning algorithms where small changes in the training set result in large changes in predictions. Breiman [1996a] claimed that neural networks and decision trees are examples of unstable learning algorithms.check last lines

Aren't gbdt's the most preferred/recommended learners in xgboost ? Which is contradictory considering how decision trees are apparently unstable learners.