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The term “Boosting” implies a gathering of calculations which changes a weak learner to strong learner. It is an ensemble technique for improving the model predictions of some random learning algorithm. It trains weak learners consecutively, each attempting to address its predecessor. There are three kinds of boosting in particular, namely, AdaBoost that assigns more weight to the incorrectly classified data that would be passed on to the next model, Gradient Boosting which uses the residual errors made by previous predictor to fit the new predictor, and Extreme Gradient Boosting which overcomes drawbacks of Gradient Boosting by using parallelization, distributed computing, out-of-core computing, and cache optimization.