Chapter 11. Creating Ensembles and Multiclass Classification

"This is how you win ML competitions: you take other people's work and ensemble them together."                                                                                        - Vitaly Kuznetsov, NIPS2014

You may have already realized that we have discussed ensemble learning. It is defined by www.scholarpedia.org as "the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem". In random forest and gradient boosting, we combined the "votes" of hundreds or thousands of trees to make a prediction. Thus, by definition, those models are ensembles. This methodology can be extended to any learner to create ensembles, which some refer to as meta-ensembles or meta-learners. We will look at one of these methods referred to as "stacking". In this methodology, we will produce a number of classifiers and use their predicted class...