Recent advances in machine learning technology make it possible to determine definitively whether or not interactions of any degree need to be included in a predictive model.
We can thus establish conclusively, for example, for a given set of predictors, that an additive model (one with no interactions) cannot be improved upon with interactions. Or alternatively, one might prove that a model with interactions will outperform a model without them.
Further, we can now identify precisely which interactions are supported by the data, and also the degree of interaction, even in very high dimensional data. The tools we use to achieve these results are extensions of Stanford University Professor Jerome Friedman's TreeNet, developed by the authors and embedded in the Salford Systems TreeNet 2.0 Pro Ex product.
Steinberg illustrates the concepts in the context of a real world regression model where we are quickly able to identify all the important interactions with a modest number of boosted tree ensemble models.
Dan Steinberg is the President of Salford Systems. He founded the company in 1982 just after receiving his Ph.D. in Economics at Harvard. Steinberg also served as a Member of Technical Staff at AT&T Bell Laboratories, Assistant Professor of Economics at the University of California, San Diego, and has participated in dozens of consulting projects for Fortune 100 clients.
He has been honored by the SAS User's Group International (SUGI) and led the modeling teams that won the KDDCup 2000 and the 2002 Duke/Teradata Churn modeling competition.
Dr. Steinberg has published articles in statistics, econometrics, computer science, and marketing journals, and has been a featured data mining issues speaker for the American Marketing Association, American Statistical Association, the Direct Marketing Association and the Casualty Actuarial Society.