Introduction
Welcome to the module on Boosting!
Boosting is one of the most powerful ideas introduced in the field of machine learning in the past few years. It was first introduced in 1997 by Freund and Schapire in the popular algorithm, AdaBoost. It was originally designed for classification problems. Since its inception, many other boosting algorithms have been developed that tackle regression problems also and have become famous as they are used in the top solutions of many Kaggle competitions. We will go through the concepts of the most popular boosting algorithms – AdaBoost, Gradient Boosting and XGBoost in this module.
In the previous course of Tree models, we had gone through different ensemble models. In this particular course we will focus on one such Ensemble model – Boosting.
In this session
This session will introduce you to the following topics:
- Ensemble models
- Introduction to Boosting
- Building blocks of Boosting
- AdaBoost procedure
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded.