From bagging to boosting

In Chapter 3, Bagging with Random Forests, you learned why ensemble machine learning algorithms such as random forests make better predictions by combining many machine learning models into one. Random forests are classified as bagging algorithms because they take the aggregates of bootstrapped samples (decision trees).

Boosting, by contrast, learns from the mistakes of individual trees. The general idea is to adjust new trees based on the errors of previous trees.

In boosting, correcting errors for each new tree is a distinct approach from bagging. In a bagging model, new trees pay no attention to previous trees. Also, new trees are built from scratch using bootstrapping, and the final model aggregates all individual trees. In boosting, however, each new tree is built from the previous tree. The trees do not operate in isolation; instead, they are built on top of one another.

Introducing AdaBoost

AdaBoost is one of the earliest and most popular...