Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision
Predictive Data Mining with Multiple Additive Regression Trees
Jerome H. Friedman
Stanford University
Monday, April 15th, 2002, 4:45PM
Gates B01 http://robotics.stanford.edu/ba-colloquium/
Abstract
Predicting future outcomes based on past observational data is a
common application in data mining. The primary goal is usually
predictive accuracy, with secondary goals being speed, ease of use,
and interpretability of the resulting predictive model. New automated
procedures for predictive data mining, based on "boosting" (CART)
decision trees, are described. The goal is a class of fast
"off-the-shelf" learning procedures that are competitive in accuracy
with more customized approaches, while being fairly automatic to use
(little tuning), and highly robust especially when applied to less
than clean data. Tools are presented for interpreting and visualizing
these multiple additive regression tree (MART) models.
About the Speaker
Jerome H. Friedman is a professor of statistics at Stanford
University, and leader of the Computation Research Group at the
Stanford Linear Accelerator Center. He recieved his bachelor and Ph.D
degrees from the University of California at Berkeley, both in
physics. His main research interests are in machine learning and data
mining.
Contact: bac-coordinators@cs.stanford.edu
