In this chapter, we looked at the major ensemble methods and their implementations in scikit-learn. It is clear that there is a large space to work in and finding what techniques work best for different types of problems is the key challenge. We saw that the problems of bias and variance each have their own solution, and it is essential to understand the key indicators of each of these. Achieving good results usually involves much experimentation, and using some of the simple techniques described in this chapter, you can begin your journey into machine learning ensembles.
In the next and last chapter, we will introduce the most important topic—model selection and evaluation—and examine some real-world problems from different perspectives.
