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Gradient Boosted Decision Trees on Hadoop
This video was recorded at NIPS Workshops, Whistler 2010. Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate models. However, most implementations today are computationally expensive and require all training data to be in main memory. As training data becomes ever larger, there is motivation for us to parallelize the GBDT algorithm. Parallelizing decision tree training is intuitive and various approaches have been explored in existing literature. Stochastic boosting on the other hand is inherently a sequential process and have not been applied to distributed decision trees. In this paper, we describe a distributed implementation of GBDT that utilizes MPI on the Hadoop grid environment as presented by us at CIKM in 2009.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
