Boosting is an ensemble learning method that trains models sequentially so that later models focus on correcting errors made by earlier ones. It is often used to reduce bias and improve predictive performance by combining multiple weak learners into a stronger model.
A machine learning ensemble metaheuristic for primarily reducing bias (as opposed to variance), by training models sequentially, each one correcting the errors of its predecessor.
A machine learning technique that iteratively combines a set of simple and not very accurate classification models (referred to as "weak classifiers") into a classification model with high accuracy (a "strong classifier") by upweighting the examples that the model is currently misclassifying. See Gradient Boosted Decision Trees? in the Decision Forests course for more information.
In model training: Boosting can refer to data augmentation techniques used to increase the size and diversity of training datasets. This is done by transforming existing examples to create additional, varied examples, which can improve model performance, especially when the original dataset is limited.