  General regression and classification models are constructed as linear
combinations of simple rules derived from the data. Each rule consists of a
conjunction of a small number of simple statements concerning the values of
individual input variables. These rule ensembles are shown to produce
predictive accuracy comparable to the best methods. However, their principal
advantage lies in interpretation. Because of its simple form, each rule is easy
to understand, as is its influence on individual predictions, selected subsets
of predictions, or globally over the entire space of joint input variable
values. Similarly, the degree of relevance of the respective input variables
can be assessed globally, locally in different regions of the input space, or
at individual prediction points. Techniques are presented for automatically
identifying those variables that are involved in interactions with other
variables, the strength and degree of those interactions, as well as the
identities of the other variables with which they interact. Graphical
representations are used to visualize both main and interaction effects.
