Boosting: meaning, definitions and examples

machine learning

Boosting is an ensemble learning technique that aims to combine the predictions of several base estimators in order to improve the accuracy of the model. It works by training a sequence of weak learners, where each learner corrects the mistakes made by its predecessor. The final prediction is then made by combining the predictions of all the weak learners.

AdaBoost, Gradient Boosting, ensemble learning

Examples of usage

• Boosting algorithms such as AdaBoost and Gradient Boosting are popular in the machine learning community.

• By iteratively adjusting the weights of misclassified samples, boosting can produce highly accurate models.

• In boosting, the emphasis is on learning from the mistakes of previous models to improve overall performance.