Dan Steinberg's Blog
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Dan Steinberg's Blog

Dan Steinberg, President and Founder of Salford Systems, is a well respected member of the statistics and econometrics communities. In 1992, he developed the first PC-based implementation of the original CART procedure, working in concert with Leo Breiman, Richard Olshen, Charles Stone and Jerome Friedman. In addition, he has provided consulting services on a number of biomedical and market research projects, which have sparked further innovations in the CART program and methodology.
Dr. Steinberg received his Ph.D. in Economics from Harvard University, and has given full day presentations on data mining for the American Marketing Association, the Direct Marketing Association and the American Statistical Association. A book he co-authored on Classification and Regression Trees was awarded the 1999 Nikkei Quality Control Literature Prize in Japan for excellence in statistical literature promoting the improvement of industrial quality control and management.

Random Forests OOB vs. Test Partition Performance

Random Forests is the unique learning machine that has no need of an explicit test sample because of its use of bootstrap sampling for every tree. This ensures that every tree in the forest is built on about 63% of the available data, leaving the remaining approximately 37% for testing [the OOB (out-of-bag) data].

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Unsupervised Learning and Cluster Analysis with CART

CART in its classification role is an excellent example of "supervised" learning: you cannot start a CART classification analysis without first selecting a target or dependent variable. All partitioning of the data into homogeneous segments is guided by the primary objective of separating the target classes. If the terminal nodes are sufficiently pure in a single target class the analysis will be considered successful even if two or more terminal nodes are very similar on most predictor variables.

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