AdaBoost

AdaBoost is one of the historically most important methods in the boosting family. Each new model focuses more heavily on examples that were misclassified in previous rounds. This allows a sequence of weak learners to become a strong classifier when combined. However, because it can be sensitive to noisy data and outliers, it should be used carefully depending on the structure of the problem.