  Generative models for classification use the joint probability distribution
of the class variable and the features to construct a decision rule. Among
generative models, Bayesian networks and naive Bayes classifiers are the most
commonly used and provide a clear graphical representation of the relationship
among all variables. However, these have the disadvantage of highly restricting
the type of relationships that could exist, by not allowing for
context-specific independences. Here we introduce a new class of generative
classifiers, called staged tree classifiers, which formally account for
context-specific independence. They are constructed by a partitioning of the
vertices of an event tree from which conditional independence can be formally
read. The naive staged tree classifier is also defined, which extends the
classic naive Bayes classifier whilst retaining the same complexity. An
extensive simulation study shows that the classification accuracy of staged
tree classifiers is competitive with that of state-of-the-art classifiers and
an example showcases their use in practice.
