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      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2010 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2010 Working Papers
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      Random model trees: an effective and scalable regression method

      Pfahringer, Bernhard
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      Pfahringer, B. (2010). Random model trees: an effective and scalable regression method. (Working paper 03/2010). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/4056
      Abstract
      We present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction. An extensive empirical investigation shows that Random Model Trees produce predictive performance which is competitive with state-of-the-art methods like Gaussian Processes Regression or Additive Groves of Regression Trees. The training

      and optimization of Random Model Trees scales better than Gaussian Processes Regression to larger datasets, and enjoys a constant advantage over Additive Groves of the order of one to two orders of magnitude.
      Date
      2010-06
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      03/2010
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2010 Working Papers [7]
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