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Random Forests Tree Ensembles: Salford Systems Exclusive Insight
Date:1/31/2013

ng explored and discussed.

According to Cutler, "The Random Forests collaboration and my real start to working in [decision] trees came in a cab. It was a stretch limo actually! I was going to a conference and I had a habit of bumping into him at the airport attending all of these conferences on neural nets. We were trying to hail a cab to the conference hotel and they didn't have a cab available so they gave us a stretch limo at the regular rate. He was telling me about some work he was doing, and it was the early Random Forests. And I started telling him about some of the experiments that I'd been doing that were using [decision] trees. So, we took my project at the time, Perfect Random Trees, and his project Random Forests and immediately stopped working on everything else and began collaborating on RF." (Audio Clip)

Random Forests is widely available now and is documented as an excellent benchmark tool for data scientists and analysts. Much of the insight provided by Random Forests is generated by methods applied after the trees are grown and include new technology for identifying clusters or segments in data as well as new methods for ranking the importance of variables. The method was developed by Leo Breiman and Adele Cutler of the University of California, Berkeley, and is licensed exclusively to Salford Systems. Ongoing research is being undertaken by Salford Systems in collaboration with Professor Adele Cutler , the surviving co-author of Random Forests. Random Forests is a collection of many CART trees that are not influenced by each other when constructed. The sum of the predictions made from decision trees determines the overall prediction of the forest. T
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