A word about ensemble models
Before you start diving into the algorithms, there is an important modeling concept that you should be aware of – ensemble. The term ensemble is used to describe methods that use multiple algorithms to create a model.
A regular algorithm that does not implement ensemble methods will rely on a single model to train and predict the target variable. That is what happens when you create a decision tree or regression model. On the other hand, algorithms that do implement ensemble methods will rely on multiple models to predict the target variable. In that case, since each of these models might come up with a different prediction for the target variable, ensemble algorithms implement either a voting (for classification models) or averaging (for regression models) system to output the final results. Table 6.2 illustrates a very simple voting system for an ensemble algorithm composed of three models.
