prone to overfitting as they can model highly complex relationships. low bias, but high variance. ensemble of weaker learners (random forests, gradient boosting) increases the bias for each tree, but weaker learner has lower variance and combats overfitting. ideally the aggregate vote of all trees will still have low enough bias and low variance.