In this chapter, we deviated from our usual pattern of learning a new type of model and instead focused on techniques to build ensembles of models that we have seen before. We discovered that there are numerous ways to combine models in a meaningful way, each with its own advantages and limitations. Our first technique for building ensemble models was bagging. The central idea behind bagging is that we build multiple versions of the same model using bootstrap samples of the training data. We then average the predictions made by these models in order to construct our overall prediction. By building many different versions of the model we can smooth out errors made due to overfitting and end up with a model that has reduced variance.
A different approach to building model ensembles uses all of the training data and is known as boosting. Here, the defining characteristic is to train a sequence of models, but each time we weigh each observation with a different weight depending on whether...