The Boosting Approach to Machine Learning

Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. This is one of the most powerful techniques for building predictive models. It can help improve algorithm accuracy and the robustness of a model. Ensemble learning uses hundreds to thousands of models of the same algorithm that work together to find the correct classification. This can be achieved by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. Models are added until the training set is predicted perfectly or a maximum number of models are added.