• Boosting is an additive model, but is different from generalized additive model, in which each weak learner only involves one variable and p number of functions are used and added up.
• Boosting is also different from random forests, another additive model. In random forests, each tree is generated independently, so they can’t borrow information from each other.
• Adaboost is a special case of this framework with exponential loss for classification.

Link to project 3