Introduction to Adaptive Boosting Classifier

Adaptive Boosting Classifier is an ensemble classifier developed by Yoav Freund and Robert Schapire. This algorithm works by creating a prediction model in the form of a set of weak models. It requires specifying a set of weak learners before actually starting it. The weight of each model is determined based on whether it correctly predicted the sample or not. In a situation where the learner has predicted wrong, his weight is slightly reduced. The whole process is carried out until convergence[1].