# Project 3: Adaboost Algorithm

- 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.