Boosting Algorithm (AdaBoost and XGBoost)
Boosting is an ensemble method of converting weak learners into strong learners. Weak and strong refer to a measure how correlated are the learners to the actual target variable[^1]. In boosting, each training sample are used to train one unit of decision tree and picked with replacement over-weighted data. The trees will learn from predecessors and updates the residuals error.