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      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2015 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2015 Working Papers
      • View Item
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      Classification and regression algorithms for WEKA implemented in Python

      Beckham, Christopher J.
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      Beckham, C.J. (2015). Classification and regression algorithms for WEKA implemented in Python. (Working paper 02/2015). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/9773
      Abstract
      WEKA is a popular machine learning workbench written in Java that allows users to easily classify, process, and explore data. There are many ways WEKA can be used: through the WEKA Explorer, users can visualise data, train classifiers and examine performance metrics; in the WEKA Experimenter, datasets and algorithms can be compared in an automated fashion; or, it can simply be invoked on the command-line or used as an external library in a Java project.
      Date
      2015-10
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      02/2015
      Publisher
      University of Waikato, Department of Computer Science
      Rights
      © 2015 Christopher J. Beckham
      Collections
      • 2015 Working Papers [2]
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