**
Predictive Learning via Rule Ensembles**

**
Jerome H. Friedman***

Stanford University

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.
* Joint work with Bogdan Popescu