Introduction to Boosting Boosting is a type of machine learning algorithm in which a strong learner (a classifier with stron predictions) is made by adding together many weak learners (classifiers with weak predictions). The idea of strong learners and weak learners are fundamental to understanding boosting, and so I will attempt to very simply summarize them below. A weak learner is classifier which predicts with slightly higher accuracy than just randomly predicting. Generally, weak learners have a very simple structure. For example, for simplicity let us consider two categories: male and female. Let us attempt to find a classifier which can predict a person's biological sex (not gender, though correlations may or may not exist), based on the following information: Features of a person: (age, height, social security number, the number of times a dress or skirt was worn in the past five years) Obviously, none of the information above will directly tell you the biological se...
