This chapter introduced a new family of methods that essentially turns the question around and asks: “How do different target segments generate feature values?” They attempt to model how the data was generated. In the use phase, when faced with a new example to be classified, they use the models to answer the question: “Which class most likely generated this example?” Thus, in data science this approach to modeling is called generative. The large family of popular methods known as Bayesian methods, because they depend critically on Bayes’ Rule, are usually generative methods.
