Chapter 13. Ensemble learning

This chapter is the concluding chapter of all the learning methods we have learned from Chapter 5, Decision Tree based learning. It is only apt to have this chapter as a closing chapter for the learning methods, as this learning method explains how effectively these methods can be used in a combination to maximize the outcome from the learners. Ensemble methods have an effective, powerful technique to achieve high accuracy across supervised and unsupervised solutions. Different models are efficient and perform very well in the selected business cases. It is important to find a way to combine the competing models into a committee, and there has been much research in this area with a fair degree of success. Also, as different views generate a large amount of data, the key aspect is to consolidate different concepts for intelligent decision making. Recommendation systems and stream-based text mining applications use ensemble methods extensively.

There...