The Supervised Learning Workshop

6. Ensemble Modeling

Overview

This chapter examines different ways of performing ensemble modeling, along with its benefits and limitations. By the end of the chapter, you will be able to recognize the underfitting and overfitting of data on machine learning models. You will also be able to devise a bagging classifier using decision trees and implement adaptive boosting and gradient boosting models. Finally, you will be able to build a stacked ensemble using a number of classifiers.

Introduction

In the previous chapters, we discussed the two types of supervised learning problems: regression and classification. We looked at a number of algorithms for each type and delved into how those algorithms worked.

But there are times when these algorithms, ...