As we progressed through the earlier chapters of this book, you learned how to apply a number of new techniques. We developed our use of several advanced machine learning algorithms and acquired a broad range of companion techniques used to enhance your use of learning techniques via more effective feature selection and preparation. This chapter seeks to enhance your existing technique set using ensemble methods: techniques that bind multiple different models together to solve a real-world problem.
Ensemble techniques have become a fundamental part of the data scientist's toolset. The use of ensembles has become common practice in competitive machine learning contexts, and ensembles are now considered an indispensable tool in many contexts. The techniques that we'll develop in this chapter give our models an edge in performance, while increasing their robustness to underlying data change.
We'll examine a series of ensembling options, discussing...