Machine Learning with R Cookbook - Second Edition

Ensemble Learning

In this chapter, we will cover the following topics:

• Using the Super Learner algorithm
• Train and test Super Learner
• Using ensemble to train and test
• 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