boosting
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 mi
Plain English Explanation
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.
How is it used?
Practitioners refer to boosting when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.