Summary

In this chapter, we have covered the ensemble learning methods of Machine learning. We covered the concept of the wisdom of the crowd, how and when it is applied in the context of Machine learning, and how the accuracy and performance of the learners are improved. Specifically, we looked at some supervised ensemble learning techniques with some real-world examples. Finally, this chapter has source code examples for the gradient boosting algorithm using R, Python (scikit-learn), Julia, and Spark Machine learning tools and recommendation engines using the Mahout libraries.

This chapter covers all the Machine learning methods and in the last chapter that follows, we will cover some advanced and upcoming architecture and technology strategies for Machine learning.