Practical Guide to Boosting Algorithms In Machine Learning

Use weak learners to create a stronger one

Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. Firstly, a model is built from the training data. Then the second model is built to correct the errors present in the first model. This procedure is continued, and models are added until either the…