Making Sense of Gradient Boosting in Classification: A Clear Guide

Introduction

Machine learning algorithms require more than just fitting models and making predictions to improve accuracy. Most winning models in the industry or in competitions have been using Ensemble Techniques or Feature Engineering to perform better.

Ensemble techniques, in particular, have gained popularity because of their ease of use compared to Feature Engineering. Multiple ensemble methods have proven to increase accuracy when used with advanced machine learning algorithms. One such method is Gradient Boosting. While Gradient Boosting is often discussed as if it were a black box, in this article, we’ll unravel the secrets of Gradient Boosting step by step, intuitively and extensively, so you can understand how it works.

In this article, we’ll cover the following topics:

• What Is Gradient Boosting?
• Gradient Boosting in Classification
  • An Intuitive Understanding: Visualizing Gradient Boosting
  • A Mathematical Understanding
• Implementation of Gradient Boosting in Python
• Comparing and Contrasting AdaBoost and Gradient Boost
• Advantages and Disadvantages of Gradient Boost
• Conclusion