Boosting.md (687B)
1 # Boosting 2 3 ML D5 4 5 **Definition:** Boosting is the process of combining several weak learners into one strong learner. 6 7 The idea of this is to sequentially train predictors to correct the output of prior models. 8 9 Adaboost is a popular boosting algorithm which is short for adaptive boosting. 10 11 There is also gradientboosting which is popular as well. 12 13 The main difference between boosting and most voting classification implementations is that it is purely sequential. It also uses weaker learners like shallow decision trees to make predictions. Additionally, where the name comes from, models boost the importance of training examples to focus the model on mproving misclassified data.