R: Recipes for Analysis, Visualization and Machine Learning

Chapter 7. Ensemble Learning

In this chapter, we will cover the following topics:

• Classifying data with the bagging method
• Performing cross-validation with the bagging method
• Classifying data with the boosting method
• Performing cross-validation with the boosting method
• Classifying data with gradient boosting
• Calculating the margins of a classifier
• Calculating the error evolution of the ensemble method
• Classifying the data with random forest
• Estimating the prediction errors of different classifiers

Introduction

Ensemble learning is a method to combine results produced by different learners into one format, with the aim of producing better classification results and regression results. In previous chapters, we discussed several classification methods. These ...