2008/09/01 by Jerome H. Friedman, Bogdan Popescu, Bogdan E. Popescu · 6 citations
Computer Science · Mathematics · #Artificial intelligence #Computer science #Degree (music) #Interpretation (philosophy) #Machine Learning and Data Classification #Machine learning #Mathematics #Neural Networks and Applications #Relevance (law) #Simple (philosophy) #Space (punctuation) #Statistical Methods and Inference #Variable (mathematics) #stat.AP
paper · pdf · doi:10.1214/07-aoas148
published as Annals of Applied Statistics 2008, Vol. 2, No. 3, 916-954 · Published in at http://dx.doi.org/10.1214/07-AOAS148 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2008/09/01 · arxiv created 2008/11/11 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.