A (small) introduction to Boosting

Boosting is a machine learning meta-algorithm that aims to iteratively build an ensemble of weak learners, in an attempt to generate a strong overall model. For example, consider a problem of binary classification with approximately 50% of samples belonging to each class. Random guessing in this case would yield an accuracy of around 50%. So a weak learner would be any algorithm, however simple, that slightly improves this score – say 51-55% or more. Usually, weak learners are pretty basic in nature.