This image contrasts the two ensemble strategies discussed in Boosting vs Bagging: bagging builds each tree independently from a bootstrapped sample of the training data and averages the resulting predictions, while boosting builds trees sequentially, with each new tree trained on the residual errors of the trees built before it. Source: quantdare.com/what-is-the-difference-between-bagging-and-boosting/