Unlike classification models, classification trees do not suffer from overfitting in the event that a large B value is used. However, this requires choosing "a value of B sufficiently large that the error has settled down" (ISLR p.317).

University of Michigan - Ann Arbor

Similar to bagging for regression trees, bagging can be extended to classification problems as well. There exist various approaches, but the most simple involves picking the "majority vote" using $B$ trees for each test observation, which represents the most common class among those $B$ predictions.

Bagging for Classification Trees

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

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