Boosting is an ensemble learning technique whereby an ensemble of (weak) models is created sequentially, with each model designed to perform better on data where the previous model made errors. The ensemble prediction is a weighted combination of the weak models, with weights defined according to the accuracy of the corresponding models on the training set.