So far, we have looked at a number of interesting machine learning algorithms, from classic methods such as linear regression to more advanced techniques such as deep neural networks. At various points, we pointed out that every algorithm has its own strengths and weaknesses—and we took note of how to spot and overcome these weaknesses.
However, wouldn't it be great if we could simply stack together a bunch of average classifiers to form a much stronger ensemble of classifiers?
In this chapter, we will do just that. Ensemble methods are techniques that bind multiple different models together in order to solve a shared problem. Their use has become a common practice in competitive machine learning—making use of an ensemble typically improves an individual classifier's performance by a small percentage.
Among these techniques...
