Boosted trees are an ensemble learning method where multiple decision trees are combined to improve model accuracy. Trees are built sequentially with each new tree trying to correct the errors made by the previous trees. This process of boosting allows for the creation of a strong predictive model from a collection of weaker models (the individual decision trees).